Metal-oxide-semiconductor field-effect transistors (MOSFETs) are the key components of silicon ICs (integrated circuits). Due to the potential of these devices to provide large, label-free, low-cost, disposable arrays of sensors that are easily integrated in portable 'point­of-care' instrumentation, efforts have been devoted worldwide, overthe past three decades, towards exploitation of field-effect mechanism in biological sensors. Most of this work concerned the development of the ion-sensitive field-effect transistor (ISFET). A major advantage of ISFET is that it operates under equilibrium conditions. Current does not flow across the biological layer due to the presence of the insulating layer on top of the semiconductor. Although ISFET is primarily a pH sensor, creation of a gate potential by enzyme-promoted reactions of analyte biomolecules leading to pH changes near its gate surface, have resulted in the development of a diversity of biosensors for medical applications. In this paper, sensing mechanisms of different biosensors based on ISFET structure for medical diagnostics are described. In order to obtain a measuring signal, the ISFET has to be associated with an interface and readout circuit. During measurements with ISFET, the drain-source voltage and the drain-source current are maintained constant while the changes in gate-source voltage are determined. Introducing the source-drain follower circuit, the Wheatstone bridge method for eliminating the influence of temperature on measurements is explained. The paper presents an overview of the current scenario and ongoing developments covering from the sensing device to the instrument. ISFET fabrication at CEERI, Pilani is briefly presented, and significant achievements are highlighted.

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The study of heart rate variability (HRV) provides a mean for observing the heart's ability to respond to normal regulatory signals that affect its rhythm. The HRV analysis has proven useful in diagnosis, treatment and monitoring of various pathologies. The modern field of HRV processing is extremely diverse, involving many areas like spectral estimation, system modeling, nonlinear dynamics and chaotic analysis, etc. With the recognition of significant relationship between the autonomic nervous system and cardiovascular mortality, efforts for development of autonomic activity have led to the use of HRV as one of the most promising markers. Thus, there is an urgent need to keep a track of advancements and activities taking place in this emerging field. This paper gives a bibliographical survey and general backgrounds of research and development in the field of HRV based on over 83 published articles. The collected literature has been divided into many sections so that new researchers do not face any difficulty for obtaining literature in this field.

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Electronics Division, Bhabha Atomic Research Centre has developed a novel instrument, called Medical Analyzer, for studying the time domain variations in the physiological parameters such as heart rate, respiration rate, stroke volume, cardiac output and peripheral blood flow in human subjects. This system has been used to explore the possibility of disease characterization from variability of heart rate, peripheral blood flow etc in around 200 of subjects with encouraging results. Also therapeutic response has been observed with material-less medicine in a limited sense. Central Council for Research in Homeopathy (CCRH) wanted to conduct systematic proving of the medicines to act as a template during patient management and therefore more powerful processing tools, to suit these investigations were required. This paper details about the requirement for this study and development of processing tools for assessment of therapeutic response by physiological variability.

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Two algorithms are presented in this paper for the detection of QRS-complexes in Electrocardiogram (ECG). The first uses single lead ECG at a time for the detection of QRS­-complexes, while the second uses 12-lead simultaneously recorded ECG. The ECG signal is filtered using digital filtering techniques to remove power line interference and base line wander. Support Vector Machine (SVM) is used as a classifier for detection of QRS­-complexes in ECG. Using the standard CSE data-set 3, both the algorithms performed highly effectively. The performance of the algorithm with sensitivity (Se) of 99.70% and positive prediction (+P) of 97.75% is achieved when tested using single lead ECG. It improves to 99.93% and 99.13% respectively for simultaneously recorded 12-lead ECG signal. The percentage of false positive and false negative is low. The proposed algorithm performs better as compared with published results of other QRS-detectors tested on the same database.

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A combination of histogram and heuristic rule based approaches are used for detection of different patterns of ECG waves and extraction of few important time -plane features, which are used for ECG interpretation and classification. At first, an efficient, simple and new approach for detection of base line and QRS complexes from the horizontal and verti­cal histogram of ECG images plotted on computer screen is described. The vertical and horizontal histograms are generated from the computation of number of ordinates for a particular abscissa at the vertical and horizontal direction respectively. The base line is determined at the maximum of the horizontal histogram whereas QRS or R peaks are deter­mined from the local maximas of the maximum area zone of vertical histograms. A high accuracy is achieved for both the cases (99.5% for QRS and 92% for base line). This method is advantageous for onscreen pictorial analysis because both QRS and base lines can be determined directly from computer screen ECG data without use of complex mathematical models, even when ECGs are tilted due to respiration and disturbed in the presence of power line oscillation. After detection of R points and base points, an efficient algorithm is developed using priory knowledge of different patterns of ECG wave for detection of P, Q, R, S and T waves and their different attributes which are useful for ECG interpretation.

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This paper proposes an approach for automatic detection of Ground glass pattern, a lung disease, from Computed Tomography (CT) and High Resolution Computed Tomography (HRCT) scans of the lung. The algorithm is based on frequency spectrum analysis of the image using Gabor filter bank. The tasks are completed in three steps: Preliminary mask formation, Peripheral mask formation and finally post processing. By these higher sensitivity and selectivity may be achieved with fast processing time. In the post processing, binary noise removal technique is used to remove noise from the detection mask.

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An instrument has been developed at Electronics Division, BARC for the non-invasive assessment of Blood Glucose, using the principle of Photo Plethysmography. In this instrument an infrared light of wavelength around 1300 nm is made incident on the index finger of the subject and the transmitted light is received by a photo-diode and detected by electronic circuit. It has been observed that the output of photo-detector is related to the blood glucose content of the subject. This output is processed further to display the blood glucose content on the LCD panel. The development of this instrument and the results obtained are described in this paper.

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Bioimpedance sensing is a noninvasive technique for measuring parameters related to tissue structure or physiological events. Generally, the impedance is sensed by injecting a high frequency low intensity current through a pair of electrodes placed across the selected region of the body and monitoring the voltage developed across the same or another pair of electrodes. The base value of the impedance and its variation can be used, with the help of an appropriate model, for obtaining diagnostic information. For testing and calibration of instruments developed for bioimpedance sensing, we have developed an impedance simulator by using a microcontroller and analog switches. It can be used for measuring sensitivity and frequency response for bioimpedance signals, and for studying the effect of various electrode configurations and common mode interference caused by bioelectric sources and external pickups.

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