The phonocardiograph (PCG) can provide a noninvasive diagnostic ability to the clinicians and technicians to compare the heart acoustic signal obtained from normal and that of pathological heart (cardiac patient). This instrument was connected to the computer through the analog to digital (A/D) converter. The digital data stored for the normal and diseased (mitral valve regurgitation) heart in the computer were decomposed through the Coifman 4th order wavelet kernel. The decomposed phonocardiographic (PCG) data were tested by backpropagation artificial neural network (ANN). The network was containing 64 nodes in the input layer, weighted from the decomposed components of the PCG in the input layer, 16 nodes in the hidden layer and an output node. The ANN was found effective in differentiating the wavelet components of the PCG from mitral valve regurgitation confirmed person (93%) to normal subjects (98%) with an overall performance of 95.5%. This system can also be used to detect the defects in cardiac valves especially, and other several cardiac disorders in general.
This paper presents an effective application of backpropagation artificial neural network (ANN) in differentiating electroencephalogram (EEG) power spectra of syncopic and normal subjects. Digitized 8-channel EEG data were recorded with standard electrodes placement and amplifier settings from five confirmed syncopic and five normal subjects. The preprocessed EEG signals were fragmented in two-second artifact free epochs for calculation and analysis of changes due to syncope. The results revealed significant increase in percentage delta and alpha (p < 0.5 or better) with significant reduction in percentage theta activity (p < 0.05). The backpropagation ANN used for classification contains 60 nodes in input layer, weighted from power spectrum data from 0 to 30 Hz, 18 nodes in hidden layer and an output node. The ANN was found effective in differentiating the EEG power spectra from syncopic EEG power spectra and the normal EEG power spectra with an accuracy of 88.87% (85.75% for syncopic and 92% for normal).
In the current work, we have proposed a parallel algorithm for the recognition of Epileptic Spikes (ES) in EEG. The automated systems are used in biomedical field to help the doctors and pathologist by producing the result of an inspection in real time. Generally, the biomedical signal data to be processed are very large in size. A uniprocessor computer is having its own limitation regarding its speed. So the fastest available computer with latest configuration also may not produce results in real time for the immense computation. Parallel computing can be proved as a useful tool for processing the huge data with higher speed. In the proposed algorithm 'Data Parallelism' has been applied where multiple processors perform the same operation on different part of the data to produce fast result. All the processors are interconnected with each other by an interconnection network. The complexity of the algorithm was analyzed as Θ((n + δn) / N) where, 'n' is the length of the input data, 'N' is the number of processor used in the algorithm and 'δn' is the amount of overlapped data between two consecutive intermediate processors (IPs). This algorithm is scalable as the level of parallelism increase linearly with the increase in number of processors. The algorithm has been implemented in Message Passing Interface (MPI). It was tested with 60 min recorded EEG signal data files. The recognition rate of ES on an average was 95.68%.
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