Implementation of The Variable Data Transmission System of Abnormal ECG with Activity State
The cause of death among modern people are the highest death rate from heart disease and monitoring and management of ECG signals to cope with these heart diseases is necessary. ECG is a bio-signal that is an important criterion for determining the presence or absence of cardiac activity states. Recently, an attempt has been made to analyze and compare biological signals and physical activity information for accurate analysis and diagnosis. However, in order to measure the ECG data for a long time, a storage space of several Mbytes and a wide bandwidth for wireless transmission are required. To solve this problem, cost, time, and high-performance systems are additional required. The implemented system minimizes the amount of packets generated during wireless data transmission as well as abnormal heartbeat detection and activity information, and enables monitoring of heart activity status and activity information in real time through a smart phone. In order to evaluate the data packet transmission and restoration performance of the system implemented in this research, the MIT / BIH Arrhythmia Database 100 record was embedded in the system controller section and the packet was transmitted to the smartphone. In addition, ECG evaluation experiments were conducted according to the activity status during daily life. As a result of the performance evaluation, both experiments confirmed the data packet generated and signal restoration performance.
D. Mozaffarian et al., “Heart disease and stroke statistics-2016 update: A report from the American Heart Association”, Circulation, vol. 133, pp. e38, 2016.
Naghavi, Mohsen, et al., “Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016.” The Lancet, pp. 1151-1210, 2017.
Shimauchi, Suehiro, et al., “An analysis method for wearable electrocardiogram measurement based on non-orthogonal complex wavelet expansion.” Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE. IEEE, pp. 3973-3976, 2017.
Zheng, Kaipei, et al., “Large Area Solution Processed Poly (Dimethylsiloxane)-Based Thin Film Sensor Patch for Wearable Electrocardiogram Detection.”, IEEE Electron Device Letters 39.3, pp. 424-427, 2018.
Zhang, Qingxue, et al., “Highly wearable cuff-less blood pressure and heart rate monitoring with single-arm electrocardiogram and photoplethysmogram signals.”, Biomedical engineering online 16.1:23, 2017.
Balasubramanian, et al., “A knowledge-driven framework for ECG representation and interpretation for wearable applications.”, Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, IEEE, pp. 1018-1022, 2017.
Lee, Seolhwa, et al., “Mining biometric data to predict programmer expertise and task difficulty.” Cluster Computing., Vol. 21(1), pp.1097-1107, 2018
So, Aram, et al., “Early diagnosis dementia form clinical data by machine learning techniques.” Applied Sciences Vol. 7(7):651, 2017.
Li, Zhijun, et al., “Adaptive impedance control for an upper limb robotic exoskeleton using biological signals.”, IEEE Transactions on Industrial Electronics 64.2, pp. 1664-1674, 2017.
Tacchino, Giulia, et al., “EEG Analysis during active and assisted repetitive movements: Evidence for differences in neural engagement.”, IEEE Transactions on Neural Systems and Rehabilitation Engineering 25.6, pp. 761-771, 2017.
Singh, Balbir, et al., “A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis.”, Computational and mathematical methods in medicine 2017, 2017.
Yang, Bo, et al., “Motion prediction via online instantaneous frequency estimation for vision-based beating heart tracking.”, Information Fusion 35, pp. 58-67, 2017.
Hicks, Robert A., et al,. “Frontal alpha asymmetry and aerobic exercise: are changes due to cardiovascular demand or bilateral rhythmic movement?.”, Biological psychology 132, pp. 9-16, 2018.
Gaudet, et al,. “Classification of Upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features.” Engineering Applications of Artificial Intelligence 68, pp. 153-164, 2018.
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