Secondary Database Searching & Hidden Markov Model


Primary database search tools are effective for identifying sequence similarities. But analysis of output is difficult. So the main principle behind the development of secondary database is that by using them, we can share the structural and functional characteristics of the constituent sequences.
Different secondary databases are formed as a result of different analysis methods. HMMs, profiles, blocks, fingerprints etc are the different pattern recognition methods used in major secondary database. Some analysis methods are given below.

a) Fingerprints 

Within a sequence alignment, we can find several motifs (motif means a consecutive string of amino acids in a protein sequence, whose general character is repeated). In secondary database, we can store such motifs so that during searching it becomes easy to identify related sequences. Motifs are used to create a signature or fingerprint and stored in secondary database. The technique of fingerprinting is not commonly used.


Similar to protein fingerprinting, blocks may be used to search sequence database to find additional family members. Here blocks within the family are used to make, independent database searches .For a given sequence, the more blocks are matched, the greater possibility that the sequence belongs to that family.


Profile is a pattern recognition method in 2 databases. Profiles define which residues are allowed that given positions, which positions are highly conserved and so profiles helps in defining the full domain alignments.

d) Hidden Markov Model (HMM) 

It can determine the most likely MSA or set of possible MSAs. HMM is a probabilistic model consisting of a number of interconnecting states. HMMs have some limitations which lead to false matches.

The first approach for discovering disease related genes is the technique of positional cloning. Hence the chromosome related to the disease in question is stabled by analyzing a population of subjects. The whole process of positional cloning is time consuming.


Database of multiple alignments  

Multiple alignment database is produced to have readily available high quality alignments. The advantages of using multiple sequence alignment is database searches is that more information is used, which results in higher sensitivity compared with pair wise searches.

PSI-BLAST is a hybrid database which uses elements of both pair wise and multiple sequence alignment methods. In PSI (Position Specific Iterated) BLAST, it allows automatic arrangement of position specific sequences. Main disadvantage of PSI-BLAST is that automatic iteration may lead to errors some times.

Multiple Sequence Alignment Methods

Methods of producing Multiple Sequence Alignments (MSA):

1. Dynamic programming: 

There are different methods of producing a MSA.  The most direct method uses a dynamic programming technique to identify the globally optimal alignment solution. For proteins, the method of dynamic programming involves two set of parameters called gap penalty and substitution matrix. Here scores are assigned to the alignment of each pair of amino acids based on the similarity of amino acids' chemical properties and the evolutionary probability of mutation.

In MSA, for n-individual sequences, an n-dimensional equivalent matrix is formed in standard pairwise sequence alignment. The search space thus increases exponentially with increasing 'n'. The MSA program optimizes the sum of all the pairs of characters at each position in the alignment. It is called sum of pair score.

2. Progressive alignment construction 

Progressive alignment is the most widely used approach to multiple sequence alignments. It is also called hierarchical or tree method. It builds up a final MSA by pairwise alignments beginning with the most similar pair and progressing distantly related pair. All progressive alignment methods require two stages.  At first stage, the relationship between sequences is represented as a tree and in the second stage the MSA is built up.

The primary problem in progressive alignment is that when errors are made at any stage in growing MSA , these errors are then propagated through to the final result, Performance also degrades when all of the sequences in the set are distantly related. Progressive alignment methods are efficient enough even if we use about 1000 sequences.

3. Iterative methods:  

A major problem in the progressive alignment method is that the accuracy of alignment heavily depend on the accuracy of initial pairwise alignment. The iterative methods work similarly to progressive methods, but repeatedly realign the initial sequences and sometime add new sequences to the growing MSA.

4. Hidden Markov Models 

It can determine the most likely MSA or set of possible MSAs. HMM is a probabilistic model consisting of a number of interconnecting states. Typical HMM based methods work by representing an MSA as a partial order graph which consists of a series of nodes representing possible entries in the columns of an MSA. In this representation a column that is absolutely conserved is coded as a single node with many outgoing connections.

Multiple Sequence Alignment Definition

Multiple sequence alignment is defined as an extension of pair wise sequence alignment to incorporate (unite together) more than two sequences at a time. Multiple alignment methods try to align all of the sequence in a given query set. Multiple alignments are often used is identifying conserved sequence regions across a group of sequences. Multiple alignments can also be used to find out evolutionary details.

Aim of multiple sequence alignment

• Multiple sequence alignment is mainly used to find out the similarity between sequences of a gene family.
• Sometimes multiple sequence alignments can also be used to express the dissimilarity between a set of sequences.
• In most cases the multiple sequence alignment can accurately find out biological data.

A multiple sequence alignment is a 2 dimensional (2D) table, in which rows represent individual sequences and columns represent the residue positions. The database of multiple alignments has a great importance. The power of multiple sequence analysis lies in the ability to find out related sequences from various species and to express the degree of similarity between them. The time taken to compute an alignment; increases exponentially with the number of sequences to be aligned.

Simultaneous alignment methods and progressive alignment methods are the two common procedures in multiple sequence alignments. In simultaneous alignment, all sequences are aligned with in a set at once are very time consuming. The progressive multiple alignment methods align sequences in pairs following the branching order of a family tree.

The most similar are aligned first and more distantly related sequences are added later. By exploiting likely evolutionary relationships, progressive multiple alignment methods are less time consuming.

Local Alignment and Global Alignment in Bioinformatics


The technique of dynamic programming can be applied to produce Global alignments via Needleman-Wunsch algorithm and local alignments via the Smith-Waterman algorithm.


There are two general models to view alignments. The first model considers similarity across the full extent of the sequences (Global alignment). The second focuses on the regions of similarity in parts ofthe sequence only.(it is local alignment). A search for local similarity may produce more biologically meaningful and sensitive results than a global alignment.

Global alignment: Needleman Wunsch algorithms.

Global alignments attempt to align every residue in every sequence and they are most useful when the sequences in the query set are similar and of roughly equal size. Needleman and Wunsch algorithm is used for computing a global alignment between two sequences and it is based on dynamic programming. The algorithm proposed a maximum match path way that can be obtained computationally by applying some rules. Here cells representing identities are scored 1 and cells representing mismatches are scored 0. This process examines each cell in the matrix and finally summation of cells is started.  When this process is completed, the maximum match path way is constructed.

Thus in global alignment comparison of the two sequences over the entire length is done. The Needleman Wunsch algorithm for global alignment is time consuming to run if the sequences are long. This is a general algorithm for sequence comparison. It maximise a similarity score to give maximum score. Maximum match is the largest number of residues of one sequence that can be matched with another allowing for all possible deletions.

Local Alignment: - Smith-Waterman algorithm

Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs. Local alignment searches for regions of local similarity and need not include the entire length of the sequences. Local alignment methods are very useful for scanning databases. Smith Waterman algorithm is used for local alignments. Even if the two given sequences are dissimilar, there will be some local similarity between sequences. Smith Waterman algorithm is used to find out this local similarity.

The key feature of Smith-Waterman algorithm is that each cell in the matrix defines the end point of a potential arrangement. The algorithm thus begins by filling the edge elements with 0.0 (floating point) values. Now the remaining cells in the matrix are compared. Three functions are compared at a time and the maximum of these three is chosen. Once the matrix is complete, the highest score is located. It represents the end points of an alignment (with maximum local similarity).

In addition to these many score matrices have been devised that weight match between non-identical residues.


The MD (mutation data) score is based on the concept of point accepted mutation (PAM). 1 PAM indicates the probability of a residue mutating during a distance in which a point mutation was accepted per 100 residues. In a mutation data matrix, the amino acids are arranged by assuming that positive values represent evolutionarily conservative replacements. Within the matrix, values greater than zero indicate likely mutations, values equal to 0 are neutral (random) and values less than zero indicate unlikely mutations.


The most common task of sequence analysis is the detection of more distant relationships. BLOSUM matrices are derived in order to represent distant relationships more clearly. Here for each cluster, the sequence segments are arranged on the basis of minimum percentage identity. For each cluster the average contribution at each residue position is calculated. By setting different clustering percentages, different matrices can be produced.

Fast A and BLAST  

The fast A and BLAST programs are local similarity search methods that concentrate on finding short identical matches between sequences BLAST (Basic Local Alignment Search Tool) all segment pairs which are identical. It is also a computational programming algorithm tool to calculate local alignments. Fast A and BLAST search methods have comparatively low speed. Hence Gapped BLAST method can be used to improve search speed.

Dot Plot Sequence Alignment


Pairwise sequence alignments can only be used between two sequences at a time but they are very efficient to find out the similarities. Pairwise comparison is a fundamental process in sequence analysis, which seeks out relationships based on sequence properties. Database searching is used to find out the sequence similarity searches. Pairwise sequence alignment methods are used to find the best matching piecewise (local) or global alignments of a two query sequences. The three primary methods of producing pairwise alignments are dot-matrix methods, dynamic programming and word methods. But of these methods, dot-matrix method is the popular one for pairwise alignments, and for multiple alignments, the other two methods are commonly used.

The most basic method of comparing two sequence is a visual approach known a dot-plot. Dot lot is a biological sequence comparison plot. The dot-matrix approach is qualitative and simple.

A dot plot is a graphical method that allows the comparison of two biological sequences and identifies the regions of close similarity between them. The simplest way visualize the similarity between two protein sequences is to use a similarity matrix own as a dot-plot. From dot-plot it is easy to visually identify certain sequence features such as insertions, deletions repeats, inverted repeats etc.

There are two dimensional matrices, which have the sequences of the proteins being compared along the vertical and horizontal axes. To construct a dot-plot the two sequences are written along the top row and leftmost column of a two-dimensional matrix and a dot is placed at any point where the character in the appropriate columns matches. In some implementations the size or intensity of the dot is varied depending on the degree of similarity of the two characters, So the matrix sequence segments appear as runs of diagonal lines across the matrix. The dot plots can also be used to assess repetiveness in a single sequence.

A manner of construction of dot plot matrix is shown below. Here for identical residue we mark it as a dot.
Dot Plot

Within a dot plot two identical sequences are characterized by a single unbroken diagonal line across the plot as shown above. But two similar, but non-identical sequences will be characterized by a broken diagonal and here the interrupted region indicates the location of sequence mismatches.

A pair of distantly related sequences with fewer similarities has a much noisier plot as shown above. Dot plot helps in comparison of sequences on the basis of evolutionary relation, structural similarity, and physiochemical properties etc.

DNA Sequence Analysis in Bioinformatics

DNA Sequence Analysis in Bioinformatics:

The term DNA sequencing refers to methods for determining the order of nucleotide bases adenine (A), Thymine (T), Guanine (G) and Cytosine (C) in a molecule of DNA. In some special cases, letters besides A. T, C, and G are present in a sequence. These letters represent ambiguity. Of all the molecules sampled, there is more than one kind of nucleotide at that position. The advent of DNA sequencing has significantly accelerated biological research and helped scientific discovery in a great extent. The analysis of DNA sequence helps in many research areas such as forensic biology, biotechnology etc. With the advent of modern sequencing tools, the speed of sequences increased rapidly it helped major projects such as Human Genome Project. Sequence analysis and its collection can increase the scientists understanding of the biology of various organisms. Nowadays there are many tools and methods to provide sequence comparisons and sequence alignments. Usually it is an automated computer based examination. DNA sequence analysis basically includes the following areas.

DNA Sequence trace
A DNA sequence trace is shown below.

The rules of the International Union of Pure and Applied Chemistry (IUPAC) are as follows for representing different nucleotide bases.

• A = adenine
• C = cytosine
• G = guanine
• T = thymine
• R = G A (purine)
• Y = T C (pyrimidine)
• K = G T (keto)
• M = A C (amino)
• S = G C (strong bonds)
• NV =A T (weak bonds)
• B = GTC (all but A)
• D = GAT (all but C)
• H =AC T (all but G)
• V = G CA (all but T)
• N = A G C T (any)

a) The comparison of sequences in order to find similar and dissimilar sequence alignments.

b) The identification of gene structures, introns, exons, reading frames etc

c) Finding and comparing the point mutations or single nucleotide polymorphism (SNP) in organism.

d) Revealing the evolution and genetic diversity of organisms.

Gene Structure and DNA Sequences:

DNA sequence databases typically contain genomic sequence data which includes information about the untranslated sequences.

Features of DNA Sequence Analysis:

The main features of DNA sequence analysis are

Detecting Open Reading Frames (ORF):

ORF (Open Reading Frames) are the longest frame uninterrupted by a stop codon. Finding the end of an ORF is easier than finding its beginning. Actually ORF is used to encode a known gene and it consists of a series of DNA codons which includes an initiation codon and termination codon

Understanding the effect introns and exons: 

Introne is a sequence of DNA bases that interrupts the protein coding sequence of a gene and Exones are protein coding sequence of gene.

DNA Sequence assembly:

Another important field of sequence analysis is to determine the nucleotide sequence of a clone. Clone is actually a copied fragment of a DNA. Usually a sequence, which is a acceptable to all is produced with the help of an assembler program. The program generates the code according to weight given to each nucleotide position.

Effects of EST (Expressed Sequence Tag) data on DNA databases 

A large part of currently available DNA data is made up of partial sequences. They are called expressed sequence tags (ESTs). ESTs are randomly selected from a DNA library and are used to identify genes expressed in a particular tissue. EST production is highly automated and it results in missing bases. This gives rise to difficulties in sequence finding. ESTs are incomplete and some cases inaccurate. ESTs add a factor of faults to databases because there is always some degree of uncertainly.

EST analysis tools. 

There are many tools available for the analysis of ESTs.

a) Sequence similarity search tools.
b) Sequence assembly tools.
c) Sequence combining (clustering) tools.

Sequence similarity search tools: are used to search the similarity between sequences. In order to find the similarity of sequences different methods such as dot-plot representation etc are used.

Sequence assembly tools: When a search of databases reveals several ESTs matching with a sequence, normally the ESTs must be aligned with each other to reveal the sequence. This type of sequence alignment is to be called a sequence assembly. Ex:- TIGR assembler.

Sequence clustering tools: The main purpose of sequence clustering tools is to save the data base search time. Sequence clustering tools take a large set of sequences and divide them into clusters. A reliable and effective mechanism for clustering ESTs will save the database search time and analysis efforts. Such tools are valuable when large numbers of ESTs are generated. In Bioinformatics sequence clustering algorithms attempt to group sequences that are somehow related. For proteins homologous sequences are typically grouped into families.

Specialized Genomic Resources (Boutique Database)


The purpose of specialized resources is to focus on species - species genomics and to particular sequencing techniques. The particular aim of such a data base is the integrated view of a particular biological system.

a) UniGene
* The collection represents genes from many organisms and each cluster relating to a unique gene and including related information corresponding to the gene.
* A valuable role of UniGene is in gene discovery.
* UniGene is also used for gene mapping projects and large scale gene expression analysis.

b)TDB — The TIGR Database

* These databases containing DNA and protein sequence, gene expression, protein family information etc.
* Also the data such as taxonomic range of plants and humans, role of cellular components are also present.

c) SGD (Saccharomyces Genome Database)

* SGD is an online data resource which contain information on the molecular biology and genetics of S.cerevisiae (Budding yeast).
* This data base provides internet access to the genome, its genes and their products etc.
* SGD helps the research field by uniting together functions to perform sequence similarity search tools.
 * The illustration of genetic maps using dynamically created graphical displays make the data base user friendly.

Genome Information Resources

Genome Information Resources

DNA Sequence databases :

a) EMBL 

The EMBL Nucleotide Sequence Databases in bioinformatics is a comprehensive database of DNA and RNA sequences collected from the scientific literature and scientific applications. Also data are directly submitted from researchers and genome sequencing groups. It is the nucleotide sequence database from the European Bio-informatics Institute. The database is produced in collaboration with DDBJ(DNA Databank of Japan) and Gen Sank (USA). These groups collect a portion of total sequence data reported worldwide all new and updated entries are then exchanged between the groups. Information can be retrieved from EMBL using sequence retrieval system (SRS).
The rate of growth of DNA database is highly exponential. Normally the size of database almost doubles during a period of one year.

b) DDBJ (DNA Databank of Japan) 

DDBJ is the DNA Data Bank of Japan. It is the sole nucleotide sequence databank in Asia which is officially certified to collect, nucleotide sequences from researchers and to issue the internationally recognized accession numbers to data submitters. The primary purpose of DDBJ operations is to improve the quality of IRIS the International Nucleotide Sequence Database. It acts as collaboration with EMBL and Gen Bank. Here also data is produced, maintained and distributed at the national institute of Genetics. With the help of internet based data submission tools, sequences are collected worldwide.

c) GenBank (Genetic Sequence Databank)

Gen Bank is another DNA database and it incorporates sequences from publicly available sources. It is a database from the national center for Biotechnology Information (NCBI). It is one of the fastest growing store houses of known genetic sequences. It has a flat file structure that is an ASCII text file, readable by both humans and computers. Gen Bank database is having big size and hence Gen Bank is split into smaller discrete divisions. A Gen Bank release includes the sequence files and information derived from the database Since the Gen Bank database is split into smaller discrete divisions, fast and specific searches are possible. In addition to sequence data, Gen Bank files contain information like accession numbers and gene names, references to the published literature etc. Usually a Gen Bank includes the sequence files and the information derived from the database. Another feature of Gen bank is that it can be searched with user query sequences.

d) GSDB (Genome Sequence Data Base):  

It is produced by the National Center for Genome Resources at New Mexico. A complete collection of DNA sequences and information related to it is created, maintained and distributed by this data base. Also data are collected from producers and a quality check is done before distribution. The database is easily accessible via internet.

Secondary Databases in Bioinformatics

Secondary databases are called so because they contain the analysis results of the sequences in the primary sources. SWISS-PROT has emerged as the most popular primary source and many secondary databases are based on SWISS-PROT due to its versatility.

Need for Secondary database

Simply it is a database that contains information derived from primary sequence data It will be in the form of regular expressions (patterns), Fingerprints, profiles blocks or Hidden Markov Models. The type of information stored in each of the secondary databases is different. But in secondary databases homologous sequences may be gathered together in multiple alignments. In multiple alignments there are conserved regions that show little or no variation between the constituent sequences. These conserved regions are called motifs. Motifs reflect some vital biological role and are crucial to the structure of function of protein. This is the importance of secondary database. So by concentrating on motifs, we can find out the common conserved regions in the sequences and study the functional and evolutionary details or organisms. Some of the common secondary databases are discussed below.

a) Prosite

It was the first secondary database developed. Protein families usually contain some most conserved motifs which can be encoded to find out various biological functions. So by using such a database tool we can easily find out the family of proteins when a new sequence is searched. This is the importance of PROSITE. Within PROSITE motifs are encoded as regular expression (called patterns). Entries are deposited in PROSITE in two distant files. The first file give the pattern and lists all matches of pattern, where as the second one gives the details of family, description of biological role etc. The process used to derive patterns involves the construction of a multiple alignment and manual inspection. So PROSITE contains documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them.

b) Prints- fingerprint database

PRINTS is another secondary database. Most protein families are characterized by several conserved motifs. All of these motifs can be aid in constructing the `signatures' of different families. This principle is highlighted in constructing PRINT database. Within PRINTS motifs are encoded as un weighted local alignments. So a small initial multiple alignments are taken to identify conserved motifs. Then these regions are searched in the database to find out similarities. Results are analyzed to find out the sequences which matched all the motifs within the finger print. PROSITE and PRINTS are the only manually annotated secondary databases. Print is a diagnostic collection of protein fingerprints.

c) Blocks

The limitations of above two databases led to the formation of Block database. In this database the motifs (here called Blocks) ate created automatically by highlighting the and detecting the most conserved regions of each family of proteins. Block databases a fully automated one. Keyword and sequence searching are the two important features of this type of database. Blocks are ungapped Multiple Sequence Alignment representing conserved protein regions.

d) Profiles

Profile database is used to find out the most conserved regions in the sequence alignment. Profile is weighted to indicate modifications (in bioinformatics wording-INDELS) are allowed in the sequence. Indels may be the insertion of a new sequence or deletion from the sequence. Profiles are also known as 'weight matrices' to provide a means of detecting distant sequence relationships.

Protein Sequence Database Examples

The primary databases contain sequence data(nucleic acid or protein).

Protein sequence databases Examples.
The different protein sequence database examples are discussed below.

a. PIR (Protein Information resources): 

It is the largest, most comprehensive, annotated protein sequence database in public domain. It is a collection of sequences for investigating evolutionary relationships among proteins .The PIR database is split into four distinct sections. PIR 1 -PI R4 based on the manner in which the protein data are entered and their status. Normally the fully classified entries are given more importance and hence the entered in PIR 1. The sequences which are not fully classified are stored in PIR 2.Since the PIR entries are not fully classified they may contain redundant (excessive) information. The unverified entries are entered in PIR 3. The PIR serves the scientific community through online access, by distributing magnetic tapes etc.


It is a very helpful biological database of protein sequences. Swiss- Prot was developed by the Swiss Institute of Bioinformatics and European Bioinformatics institute. This database provides a high level of integration with other databases and has a very low level of redundancy (it means that less identical sequences are present in the database.) This database provides high level information including descriptions of the function of the proteins, its variants, structure of its domains etc. SWISS-PROT is one of the most popular protein sequence resources because of the quality of its entries. Also SWISS-PROT contains 70,000 entries from more than 5000 different species. The structure of SWISS-PROT makes computational access both straight forward and efficient. So SWISS-PROT is the most widely used protein sequence database in the world. Swiss-Prot functions as a minimal redundant information source. It means excessive data is not present- only the vital information is stored.

Swiss -prot provides descriptions of a non-redundant set of proteins including their function, domain structure, post-translational modifications and variants. It is tightly integrated with other databases. Swiss-Prot concentrates on model organisms of distinct taxonomic groups to ensure the presence of high quality annotation.

The Swiss-Prot group develops and maintains other databases including PROSITE, a data base of protein families, and ENZYME database of enzyme nomenclature.
 Structure of Swiss-Prot: Swiss-Prot emerged as a famous database due to the quality of its annotations (comments), structure and the way in which the data are stored. The common structure of database is given in table.

Two letter code in the entry
Each entry begins with an Identification line.
An additional identifier is provided by the Accession number
Give information about Date of entry, date of last modification etc. 
Description lines to describe the name by which the protein is known.
Give Gene Name.
Indicate Organism Species
Organism Classification information
R-line irovides a list of supporting references.
Comment lines to indicate the various protein details such as its function, subcellular location similarity to particular protein families etc.
These are called Database cross Reference lines to provide links to other bio-molecular databases, primary and secondary databases etc.
Give applicable Keywords
Feature table indicates the main regions of sequences concerned.
SQ line includes the sequence itself.
To indicate the end of entry.

c. TrEMBL ( Translated European Molecular Biology Laboratory). 

A special feature of TrEMBL format is that it contains translations of all coding sequences (CDS). The main aim of TrEMBL is to allow very rapid access to sequence data from genome projects. TrEMBL is a very large protein sequence database in Swiss-Prot format. It is generated by computer translation of the genomic information from the EMBL Nucleotide Sequence Database. Computer translation is not entirely perfect. So proteins predicted by the TrEMBL database can be hypothetical and many TrEMBL entries are poorly annotated (TrEMBL has two main sections designated SP-TrEMBL and REM-TrEMBL.

SP-TrEMBL: SWISS-PROT TrEMBL contains entries that are united together into Swiss-Prot. Swiss-Prot accession numbers are provided for all the entries of SP-TrEMBL.

REM-TrEMBL: Contains sequences that are not concerned to be included in SWISS-PROT.   

Composite Protein Sequence Databases:

Composite databases use a variety of different primary sources and are hence efficient to search. Different methods can be used to create composite resources. Composite databases render sequences searching much more efficient because they avoid the need to interrogate multiple sequences. The main composite databases are,

a) NRDB (Non-Redundant Database)
b) OWL

This database has the advantage of containing fewer errors than many Other composite databases. Different composite databases use different primary sources. 

Central Monitoring Console in ICU

Central Monitoring Console in ICU:

In modern central monitoring console units, the entire information of different patients from different bedside monitors are collected and displayed. With this information the operator in the central monitoring console can give guidelines to the physicians and nurses so that the patients with abnormalities in various physical parameters can be given care separately. So, all the measured physical parameters are routed from each on bedside monitors to the central nurse's console. The CMC consists of an array of multi-channel oscilloscopes, digital tachometers etc. The typical block diagram of CMC in ICU is shown below.

Here the electrodes measure different physiological parameters from different patients. They are amplified by high gain op-amps and are locally displayed on bedside monitors. Also digital readouts and paper readouts are provided. Also local alarms are provided to alert the staff if the condition of patients becomes abnormal. The various parameters from each BSM is transmitted to central monitoring console by suitable transmission paths. The staff at the central monitoring console can continuously monitor the patients and the number of staffs at the bedside monitors can be reduced. The data can also be stored in a digital computer.


In modern central monitoring console units, the entire information of different patients from different bedside monitors are collected and displayed.

The staff at the central monitoring console can continuously monitor the patients and the number of staffs at the bedside monitors can be reduced.

Intensive Care Unit and Critical Care Unit


Intensive Care Unit and Critical Care Unit or Coronary Care Unit are specific care units utilized as a part of doctor's prescription in different nations that gives intensive care medicines. Excellent ICUs are equipped with medicinal devices, for example, mechanical ventilators, bedside monitors, digital cardiotachometers, pacemakers, defibrillators, dialysis instruments and so on. The biological data related with the patient from the bedside monitors can be dissected by the concerned doctor or nurse to give better care. The quality of care of an ICU relies upon the patient to nurse ratio. For a decent ICU a proportion of 2 patients to a nurse is suggested. Likewise the different parameters from various ICU units can be sent to a central monitoring console for detailed analysis and care (figure shown).

 Bedside monitors and CMC

CCU (Critical or coronary) care unit is a unique care unit managing with the care of patients with diseases associated with heart(such as heart attack).The principle highlight of CCU is the accessibility of telemetry or the consistent monitoring of ECG in order to check the proper functionality of heart.


Cardio tachometers

The cardio tachometers are used to count the heart rate of patients. For a normal human the heart rate is 72 pulses minute. By using digital cardio tachometers which directly display the heart rate, we can examine the health of patients. The cardio tachometers can be of analog or digital type.


The block diagram representation of an analog cardio tachometer is shown below (figure).The analog cardio tachometers are not commonly used now a day. They provide a DC voltage proportional to the patients' heart rate. This DC voltage can be displayed on analog or digital voltmeter.

 The different blocks are explained below. 


The ECG of patient under test is measured by using a proper electrode and applied to a differentiator. The function of differentiator is to avoid double counting. The basic principle -of cardio tachometer is to measure the number of R-waves in the ECG. Since for each heart pulse the R-wave have the highest amplitude level, it can, provide the heart rate. But in some patients T-wave or P-wave may be predominant. It may produce double counting false. But by using differentiator we can avoid this error. This is because even if the T-wave or P-wave is large, the fast changing R-wave will always produce a large output voltage than P-wave or T-wave due to the effect of differentiator. This block is also called R-wave discriminator.

2.Level Detector 

Since each R-wave can produce a specific voltage, the output of R-wave discriminator is applied to a level detector .The level detector produce an output voltage change only when the predetermined input voltage level is exceeded.(That means it will produce an output voltage change for each R-wave).The differentiator and level detector stages are collectively called as QRS-discriminators.

3.Mono stable multivibrator 

The o/p of level detector is corresponding to no. of R-waves. The output from level detector is applied to the mono stable multi vibrator. So an output pulse is generated for each R-wave. These pulses have constant duration. But the pulse repetition rate will vary with respect to the heart rate.


Integrator averages the pulses applied to its input from the mono stable output. In a mono stable multi vibrator one o/p pulse is generated for each R-wave and the pulses have constant width. So the DC o/p voltage of integrator will be proportional to the number of R-waves per unit time.

5.Digital /analog voltmeter

Even though we are using analog technique the o/p from the integrator is applied to a digital voltmeter. For more indication the o/p from integrator can also be applied a tone indicator which produces specific tone corresponding to each heart rate and a lamp indicator in which the LED glows corresponding to the heart rate.


Here ECG is taken from patient using suitable electrode and passed through differentiator, level detector and mono stable circuits as in the case of analog cardio tachometers. The difference between analog and digital tachometer is that the o/p from the mono stable is applied to a 4- in -1 generator. The 4- in -1 generator generates 4 o/p pulses corresponding to one pulse from the mono stable multivibrator. The o p, from the 4-in -1 generator is applied to one of the AND gate inputs and the other input is the o/p from a 15 s time base. So the AND gate will be on for -15 seconds. Since AND gate will be ON for 15s, the gate will pass the pulses to a digital counter connected to its 6-1p. So the counter counts the pulses from 4-in -1 generator within 15 s. Suppose 17 pulses are generated from the mono stable within 15s.These 17 pulses will be converted in to 68 pulses by the 4- in- 1 generator.(17*4).So the counter will show the count rate as 68 pulses .After 15 seconds, the counter o/p will be displayed and updated by the 15 s time base. Sometimes the gating error of the AND gate can create certain errors in a digital cardiotachometer. Digital tachometers which can count from 27 to 199 beats per minute are available. They have less power consumption. Also accuracy can be enhanced by using a programmed read-only memory. The block diagram of Digital Cardiotachometer is shown below.


Since the heart rate is an important physiological parameter, it has to be monitored continuously. If the heart rate crosses the limit in either direction, it has to be highlighted. In cardio tachometers, alarm circuits are provided on bedside monitors to warn the staff on an emergency condition. Here the mechanical arrangement of alarm is designed in such a way that it will turn ON when heart rate is too low or too high. A metal vane connected to the, meter pointer trigger the alarm circuit when pointer moves on either side exceeding a limit. When the pointer exceeds the limit, the metal vane will blind the photocell assembly .So the resistance will increase and it causes triggering of alarm circuit. Same condition occurs when the pointer exceeds limit in opposite direction. Here also the metal vane will blind the photocell assembly to trigger alarm (figure shown below).

Advantages, Disadvantages and Applications of EEG

Advantages of EEG:

1.They are functionally fast, relatively cheap and safe way of checking the functioning of different areas of brain.

2. High precision time measurements

3.Today's EEG technology can accurately detect brain activity at a resolution of a single millisecond..
4.EEG electrodes are simply stuck onto the scalp. It is therefore a non-invasive procedure.

5.EEG equipment is relatively inexpensive compared with other devices and is simple to operate. 

Disadvantages of EEG:

1.The main disadvantage of EEG recording is poor spatial resolution.

2.The EEG signal is not useful for pin-pointing the exact source of activity. In other words they are not very exact.

3.EEG waveform does not researchers to distinguish between activities originating in different but closely adjacent locations.

Applications of EEG:
1. EEG is mainly used in studying the properties of cerebral and neural networks in neurosciences.

2. It is used to monitor the neurodevelopment and sleep patterns of infants in ICU and enable the physician to use this information to enhance daily medical care.

3. In epilepsy, EEG is used to map brain areas and to receive localization information prior to a surgery. 

4. The EEG neuro-feedback or EEG bio-feedback or EEG bio-feedback has many applications such as treating for physiological disorders and neurological disorders such as epilepsy.

5. Many disorders as chronic anxiety, depression etc can be found out using as EEG pattern.

Features of EEG:

• Hardware costs are lower when compared with other imaging techniques such as MRI scanning.

• EEG sensors can be deployed into a wide variety of environments.
• EEG allows higher temporal resolution on the order of milliseconds.

• EEG is relatively tolerable to subject movements as compared to MRI.

• The silent nature of EEG allows for better study of the responses.

• EEG can be used in subjects that are not capable of making a motor response.

• In EEG some voltage components can be detected even when the subject is not responding to stimuli.

Block Diagram of EEG Machine

The basic block diagram of an EEG machine is shown above. The function of each block in the system is described below.

1) Montage selector: 

Montages are patterns of connections between the electrodes and the recording channels. The montage selection switch is used for selecting a particular channel. Different channels convey different information. Montages are always symmetrical and hence in the 10-20 electrode placement system the electrodes are also placed symmetrically. The EEG signals are transmitted from the electrodes to the montage selector panel. The montage selector of an EEG machine is a large frame which consists of different switches so as to allow the user to select the desired electrode pair.

2) Pre-amplifier 

The function of pre-amplifiers in the EEG measuring system is clear from the name itself. As the EEG signals are having amplitude levels in microvolt range it is compulsory that they are to be amplified before further processing. It is to ensure that the information from the EEG electrodes is not affected by any external noise. We normally use high gain, high CMRR operational amplifiers as preamplifiers due to its versatile features.

3) Filters and amplifiers 

The muscle artifacts (noise) are a major problem regarding the EEG waveform. These noises can make the representation dishonest. So we have to filter out these noise contents. This function is done by a bank of filters in the EEG machine systems, which are selected according to the need. Amplifiers are used here also to improve the amplitude levels of EEG waveform.

4) Analog to Digital Converters (ADC) 

For the detailed analysis of the EEG waveform, we use computers and oscilloscopes. As the computers only accept digital data we have to convert the analog EEG information in to digital form. The function of ADC is to convert the analog EEG signal to digital form. Thus the computer can store the EEG waveform for future reference.

5) Writing recorder and paper drive 

The writing part of an EEG machine is usually consists of an ink type direct writing recorder. The recorder will be a chart paper which is driven by a synchronous motor. For the clear representation of the EEG waveform an accurate and stable paper drive mechanism is provided by the synchronous motor.  Also there are provisions to control the paper speed.

A typical EEG waveform 

The schematic of an early EEG is shown in figure above.

Electroencephalography EEG Electrode Placement System

Electroencephalography (EEG):

Simply an electroencephalogram (EEG) is the representation of electrical activity of brain. By using different types of EEG electrodes, EEG is taken and the picked up signal is conditioned to make it suitable for recording and analysis. The EEG is of prime importance in medical cases.  The signal conditioning is required because the picked up EEG signal will be having very minute amplitude levels. The different stages of EEG recording are shown below.

EEG Electrodes: 

EEG recording electrodes and their proper function are critical for obtaining high quality EEG. Many electrodes with different characteristics can be used for the EEG measurement. The types of EEG electrodes are discussed below 

1. Disposable electrodes 
2. Reusable disc Electrodes
3. Scalp electrodes. 
4. Saline based electrodes 
5. Needle electrodes. 

Commonly used scalp electrode consists of Ag-AgCl disks with long flexible leads that can be plugged in to the amplifier. Advantage of this type of scalp electrode is that they can accurately record even very slow changes in potential. When we use Ag-AgCl scalp electrodes the space between the electrode and skin should be filled with conductive paste. This paste also helps to stick. Usually needle electrodes are used for long measurements and here a needle is invasively inserted under the scalp. 

Ag-AgCl solution can also be used with disposable electrodes .Disposable EEG electrodes eliminate need to clean, maintain and store after each use. The Ag-AgCl sensor and free wires result in a consistent high quality signal. The electrodes are discarded after one time use. Photographs of reusable EEG electrode and needle electrode are shown in below figures.

Reusable Electrode

Needle Electrode


The most popular scheme used in the placement of electrode for the EEG pick up is the 10-20 electrode placement system. As the cranial area is divided into four main regions, the electrodes are placed accordingly in the different regions. Main regions of cranium are frontal , parietal, temporal and occipital regions. The electrodes placed in each region is denoted as F (Frontal ), P (Parietal ), T(Temporal) and O(Occipital). The 10-20 electrode placement system is internationally used. In this set up the head is mapped by four standard points. They are nasion, inion and left and right ears. The 10-20 electrode placement system is shown below.

In 10-20 electrode placement system we use 20 electrodes. One electrode is used for grounding the subject. The electrodes may be uni-polar or bipolar. In a unipolar arrangement, one of the electrodes is takes as common to all channels. So to measure the bioelectric potential from brain, we use any one of the nineteen electrodes and, the common electrode. Scalp electrodes or needle type of EEG electrode can be used. If electrodes are to be used under the skin needle electrodes are used. In a bipolar system any of the two electrodes is used for the measurement. Here before placing the electrodes the nasion-inion distance is measured. Then points are marked on the shaved head at 10%, 20%, 20%, 20%, 20% and 10% of this length. Then the electrode is placed on these points. Hence this system is called 10-20 electrode system. 

EEG Amplitude and frequency bands:

EEG signals are having very low voltage amplitude Ievels. The voltage ranges from (1 - 100) microvolts. These are also having very low frequencies ranging from about 1 Hz to 100 Hz. The EEG waveform differs in their shapes and they show dramatic changes to the different stages of sleep. Hence they are called EEG sleep patterns, Hence we have to use high gain, high CMRR input preamplifiers in order to boost these signals. The details of five different categories of EEG bands are shown below.
EEG Frequency bands

1 – 4 Hz
Less than 100µV
4 – 8 Hz
Less than 100µV
8 – 13 Hz
Up to 10µV
13 – 22 Hz
2 - 20µV
30 Hz or higher
Up to 2µV

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