ECG ARRHYTHMIA TIME SERIES CLASSIFICATION USING 1D-CONVOLUTION LSTM NEURAL NETWORKS

An electrocardiogram (ECG) can be dependably used as a measuring device to monitor cardiovascular function. The abnormal heartbeat appears in the ECG pattern and these abnormal signals are called arrhythmias. A faster and more accurate result can be reached by classifying and automatically detecting arrhythmia signals. Several machine learning approaches have been applied to enhance the accuracy of results and increase the speed and robustness of models. This research proposes a method based on Timeseries Classification using deep Convolutional -LSTM neural networks and Discrete Wavelet Transform to classify beats in three experiments, the first one is to classify 4 different types of Arrhythmia in the MIT-BIH Database. The second one for enhancement the first experimental results. The third one is for classifying the whole MIT-BIH database. According to the results, the suggested method gives predictions with an average accuracy of 97% in the first experiment, 99% in the second one, and 97.7% in the third experiment,without overfitting

Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning

Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors