Transfer learning in ECG classification from human to horse using a novel parallel neural network architecture

Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac innervation. Both filtering, beat detection to classification for eECGs are currently poorly or not described in the literature. There are also no public databases available for eECGs as is the case for human ECGs. In this paper we propose the use of wavelet transforms for both filtering and QRS detection in eECGs. In addition, we propose a novel robust deep neural network using a parallel convolutional neural network architecture for ECG beat classification. The network was trained and tested using both the MIT-BIH arrhythmia and an own made eECG dataset with 26.440 beats on 4 classes: normal, premature ventricular contraction, premature atrial contraction and noise. The network was optimized using a genetic algorithm and an accuracy of 97.7% and 92.6% was achieved for the MIT-BIH and eECG database respectively. Afterwards, transfer learning from the MIT-BIH dataset to the eECG database was applied after which the average accuracy, recall, positive predictive value and F1 score of the network increased with an accuracy of 97.1%

Wavelet based QRS detection in ECG using MATLAB

In recent years, ECG signal plays an important role in the primary diagnosis, prognosis and survivalanalysis of heart diseases. Electrocardiography has had a profound influence on the practice of medicine.This paper deals with the detection of QRS complexes of ECG signals using derivativebased/Pan-Tompkins/wavelet transform based algorithms. The electrocardiogram signal contains animportant amount of information that can be exploited in different manners. The ECG signal allows for theanalysis of anatomic and physiologic aspects of the whole cardiac muscle. Different ECG signals fromMIT/BIH Arrhythmia data base are used to verify the various algorithms using MATLAB software.Wavelet based algorithm presented in this paper is compared with the AF2 algorithm/Pan-Tompkinsalgorithms for signal denoising and detection of QRS complexes meanwhile better results are obtained forECG signals by the wavelet based algorithm. In the wavelet based algorithm, the ECG signal has beendenoised by removing the corresponding wavelet coefficients at higher scales. Then QRS complexes aredetected and each complex is used to find the peaks of the individual waves like P and T, and also theirdeviations.Keywords: Electrocardiogram (ECG), AF2 Algorithm, MATLAB, Pan-Tompkins algorithm, WaveletTransform, Denoisin

Wavelet Signal Processing of Physiologic Waveforms

The prime objective of this piece of work is to devise novel techniques for computer based classification of Electrocardiogram (ECG) arrhythmias with a focus on less computational time and better accuracy. As an initial stride in this direction, ECG beat classification is achieved by using feature extracting techniques to make a neural network (NN) system more effective. The feature extraction technique used is Wavelet Signal Processing. Coefficients from the discrete wavelet transform were used to represent the ECG diagnostic information and features were extracted using the coefficients and were normalised. These feature sets were then used in the classifier i.e. a simple feed forward back propagation neural network (FFBNN). This paper presents a detail study of the classification accuracy of ECG signal by using these four structures for computationally efficient early diagnosis. Neural network used in this study is a well-known neural network architecture named as multi-Layered perceptron (MLP) with back propagation training algorithm. The ECG signals have been taken from MIT-BIH ECG database, and are used in training to classify 3 different Arrhythmias out of ten arrhythmias. These are normal sinus rhythm, paced beat, left bundle branch block. Before testing, the proposed structures are trained by back propagation algorithm. The results show that the wavelet decomposition method is very effective and efficient for fast computation of ECG signal analysis in conjunction with the classifier