Signal processing electrocardiogram using wavelet transform based on mallet fast algorithm

Abstract

There has been requirement to evolve a general parallel architecture for concurrently processing Electrocardiogram (ECG) using Wavelet Transform based DSP algorithms. In order to acquire spatial ECG electrical signals, 12 ECG lead channels need to be digitized and concurrently processed by Wavelet Transforms, simultaneously acquiring from all the 12 leads. More over now a day 15 Channel ECG is also under consideration in research, for on-line monitoring of critical patient in an ICU situation, while assessing or monitoring load on heart, during treatment process. In this wavelet transform algorithms, matrix inverse operations using wavelet bases matrix, requiring simultaneous matrix multiplications are involved. In order not to loose, high frequency components of ECG, for signal perception or deviation detection using wavelets, the ECG beat is segmented into 410 cubic splines at 2048bps sample rate, to explore association of ECG deviations to different heart ailments. Each spline has associated wavelet coefficient matrix, computed using bases matrix and incoming Digitized ECG vector matrix. On each of the12 lead ECG channels, there are 410 matrix operations required per channel per beat. These matrix operations are required simultaneously, on all the 12 lead ECG Channels, for concurrent digital signal processing of channels for deviation detection. In this processing, multiple data has to be processed by single instruction or algorithm statement, which leads to SIMD (Signal Instruction Multiple Data) architecture. So, a parallel architecture with 16 Processing Elements (PE) with array multipliers for matrix operations are evolved in this review article for suggesting a general parallel architecture for ECG Wavelet Analysis for Signal Perception and Deviation Detection. Using this general parallel architecture, several Wavelet Transform based algorithms, for signal perception for deviation detection are possible, for associating ECG deviations to different heart ailments and localize the deviation to mechanical functional deviation of the hearts anatomy or muscle for on-line diagnostic purpose. So a complete architecture is evolved for such parallel processing, searching the architectural research space and suggested architecture, illustrating brief part of an algorithm.