M. A. Rashid

Development of a noninvasive continuous blood pressure measurement and monitoring system

Non invasive continuous blood pressure measurement system is more useful than conventional blood pressure measurement systems. The arterial system is extraordinarily well regulated blood delivery network in human body. It responds very quickly according to the body movements like altered body position, or in sudden excitation. So now a days continuous blood pressure monitoring devices are becoming more essential. Many types of blood pressure measurement devices are available. Those devices allow only few blood pressure readings in every 10 minutes. In contrast to them, we develop a very low cost noninvasive continuous blood pressure measurement and monitoring system. It measures blood pressure using volume oscillometric method and photoplethysmography technique during a long time period continuously. The rate of change of blood volume in an organ such as finger has a linear relationship with blood pressure. This rate of change of blood volume in finger is measured by an optical sensor network which estimates blood pressure. It displays the numerical value of systolic and diastolic blood pressure in a mini LCD. Our developed system is reliable, accurate and less expensive.

Electronic energy meter with remote monitoring and billing system

Electronic energy meter is capable of taking readings and can store it into its memory. Taking energy meter reading is time consuming and an expensive task. The meter reader travels for a long distance and take the reading manually to prepare the bill. Consumers have to go to the billing office, stand in a long line and submit the bill. This is a boring job and time consuming also. It can be avoided by remote monitoring of electronic energy meter and prepaid billing system by the use of cash card. In this paper measurement of energy, remote monitoring, preparing of bill and billing system is presented. Low cost ATMEGA8L microcontroller is used here to control the whole system. Sampling of voltage and current is done by it. Then it processes data to achieve power in that instant. Then it stores the value of total energy consumed by the consumer and can calculate energy charge according to the tariff. LCD display is attached with this system to show total energy consumed, power factor and amount of charge etc. Communication between central energy distribution office and energy meter is done through power line. Complex tariff rate set up and cash card based billing is possible in this system. Electronic meter gives high accuracy for nonlinear loads than conventional rotating disc type electro-mechanical meter. Greater accuracy and stability can be maintained in this system.

PHYSIOLOGICAL SIGNAL ANALYSIS FOR COGNITIVE STATE ESTIMATION

The purpose of this paper is to identify inconsistency in human physiological signals based on cognitive states by measuring and analyzing bio-signals. In this paper, the cognitive states are estimated using physiological signal analysis. The parameters are electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and blood pressure (BP). The signals have been collected using BIOPAC system in which the subjects were induced to undergo the specific sequence of the cognitive state. For getting physiological signals during different conditions, we utilized power point slide show, video clips and question answer method which elicits mental reactions from the subjects. Data is taken before and after four tasks that encompassed the motor action (MA), thought (TH), memory related (MR) and emotion (EM). These measured values are analyzed using BIOPAC Acknowledge software. It was found that the motor action and thought states have effects on BP while MR and EM state mainly affect the ECG measurement. The decibel value and frequency found for EM state in ECG are minimum compared to relaxed state (RS) condition. Similarly, the maximum frequency and dB value is found for MR state. No significant variation was seen for MA and TH states. Thus it was decided that the MR and EM states mainly affect the ECG measurement. For BP the value increases in MA state and decreases in TH state. The MA state mainly affects the EMG signal while other states have no significant changes. The EEG mainly detects the signal of task performed by the specific brain region where the electrodes are placed. In EEG analysis, the electrodes are placed in occipital lobe region which gives mainly the variation in alpha amplitude of EEG with eyes closed and eyes opened. Alpha wave amplitudes vary with the subjects attention to mental tasks performed with eyes closed.

Microcontroller based health care monitoring system using sensor network

Non invasive and continuous measurement of blood pressure becomes popular now a day. This is very effective way for hypertensive patients to prevent cardiovascular problems and precisely regulate anti-hypertension cures. Blood pressure can be measured by conventional cuff-based sphygmomanometer or invasive instruments in non continuous mode. In this paper, we propose the microcontroller based continuous non invasive cuff less blood pressure measurement system with an alarm circuit for health care monitoring system. Light signal is used in sensor network section of this embedded system as light does not have any harmful effect on human body when it works in continuous mode. Pulse rate calculation and body temperature determination is also embedded in this system using sensor network. Accuracy of the system is found in acceptance range by comparing the results with the existing conventional systems. If BP reading, heart rate or body temperature exceeds the standard range for any patient, the system is able to notify using an alarming circuit. The whole system is controlled by microcontroller ATMEGA8L. The overall system is reliable, accurate, portable, trust worthy, user friendly and cost effective.

Cost Effective Proposal to Produce Clinical Engineering Professional to Sustain the Health Care Services of Developing Countries Such as Bangladesh

In the recent years, health care services come under spotlight of the Clinical Engineering Professional. Hence developed countries produce skilled clinical engineers to sustain their health care management since past about 35 years as well as most developing countries. However, economically fluctuating developing countries such as Bangladesh could not produce skilled clinical engineers due to lack of awareness and high cost. As the results, most of the developed countries such as Bangladesh could sustain the standard health care services. This paper illustrates an economical approach to produce the skilled clinical engineers to improve the present health care services which faces lots of problems. Our proposal will be more cost effective to produce skilled clinical engineers for the benefit of health care services. The clinical engineers are the costly potential resources for the health care technology to sustain the health care services among the health care technology personnel. We firmly believe that the current ideas of the health care planers, health policy makers and health stakeholders about clinical engineers will be clear. Public and private hospitals will be encouraged to produce and introduce skilled clinical engineers.

Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using -NN Classifier

Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (R), and zero crossing (ZC) from the epileptic signal. The k-nearest neighbours (k-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.