Embedded system design with filter bank and fuzzy classification approach to critical cardiac abnormalities detection

Qidwai, Uvais; Shakir, Mohammed

doi:10.1109/isiea.2012.6496661

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…Many of those researchers used FIR filters, filter banks or wavelet filtering to extract the features of the QRS complex. Among many methods of ECG signal classification, researchers recently prefer to utilize fuzzy classifiers [1], [3], neural network classifiers [4], [5] or even some extreme learning machine [6]. Other researchers presented hybrid systems used as a diagnostic classifier for the ECG signals; such as a neuro-fuzzy network [7], [8] and a combined method of particle swarm with neural network [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic-Based IIR Filter Design for Efficient QRS Complex Detection using Neuro-Based Classifier

Abdul–Jabbar¹,

Saadi²

2015

(AREJ)

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In this paper, a new method is proposed for QRS complex detection using a special kind of IIR filter called bi-reciprocal lattice wave digital filter (WDF). This filter has some attractive features that make it more efficient than other types of filters. One of these features is its sufficient linear phase in the passband, thus yielding a perfect reconstraction condition. Therefore, it is not required to cascade the designed filter with an all-pass filter for correcting the phase distortion. The coefficients of the designed filter are achieved by simulating the FIR response suggested in [1]. A least square method solution is used in such simulation with a genetic-based algorithm. A simplified structure for the designed filter is accomplished with less-complex realization. 50 records of European ST -T ECG database is classified into four classes (Normal, Left Bundle Branch Block (LBBB), ST segment elevation, and Left Ventricular Hypertrophy (LVH)). By applying the designed filter coefficients into neural network classifier, the results show that the accuracy of the classification process is 95.9 %.

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