Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi


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Heart monitoring kits are only available for bedridden patients and the traditional heart monitoring kits have many wires that are obstacle patients’ mobility. Most of the existing heart monitoring kits can not detect heart diseases. Thus, the current study proposed a wireless heart monitoring kit to monitor patients with a heart abnormality. The proposed kit can detect and classify four arrhythmia types as well as normal ECG with high accuracy. The design and development of the wireless heart abnormality monitoring kit (WHAMK) in this research were divided into three stages. These stages are the development of an arrhythmias detection and classification method using artificial intelligence approach, design and implementation of the kit hardware, and design and coding of the kit software. Arrhythmias classification approach is divided into four stages, namely obtaining the electrocardiograph (ECG) signals, preprocessing, features extraction and classification. The features extraction method are based on statistical features. The library support vector machine (LIBSVM) was used to classify the ECG signals. The hardware of the kit is divided into two parts, namely ECG body sensor (EBS), and processing and displaying unit (PDU). EBS working on acquiring the ECG signal from patient's body. PDU working on processing the collected ECG signal, plotting it and detecting the arrhythmias. Arrhythmias classification approach was developed by using statistical features and LIBSVM. They were implemented in the software of the kit to enable it to detect the arrhythmias in the real-time and fully automatically. The kit can detect and classify four arrhythmia types as well as normal sinus rhythm (NSR). These types of arrhythmia are premature atrial contraction (PAC), premature ventricles contraction (PVC), Bradycardia and Tachycardia. The proposed kit gave a good accuracy for detecting and classifying Arrhythmia with the overall accuracy of 96.2%.





K. A. Alfarhan et al., "Wireless Heart Abnormality Monitoring Kit Based on Raspberry Pi", Journal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 35, pp. 96-108, 2018

Online since:

January 2018




* - Corresponding Author

[1] WHR, Coronary Heart Disease in Malaysia. World Health Rankings, (2014). Available on http: /www. worldlifeexpectancy. com/malaysia-coronary-heart-disease.

[2] Centers for Disease Control and Prevention CDC. Heart Disease Facts & Statistics. (2015). Available on http: /www. cdc. gov/heartdisease/facts. htm.

[3] Heart-Foundation-organization, Heart Disease: Scope and Impact. The Heart Foundation organization. (2014). Available on http: /www. theheartfoundation. org/heart-disease-facts/heart-disease-statistics.

[4] R. Klabunde, Cardiovascular Physiology Concepts, 2nd ed., Lippincott Williams & Wilkins, (2011).

[5] S. A. Shebi and B. C. Pillai, Design of Wi-Fi Based Mobile Electrocardiogram Monitoring System on Concerto Platform. Procedia Eng. 64 (2013) 65-73.

[6] M. Biswas, R. S. Landge, B. A. Mahajan, and S. Kore, Raspberry Pi Based Patient Monitoring System using Wireless Sensor Nodes. Int. Res. J. Eng. Technol. 3 no. 4 (2016) 1693–1696.

[7] J. Wang, T. Fujiwara, T. Kato, and D. Anzai. Wearable ECG Based on Impulse Radio Type Human Body Communication. IEEE Trans. Biomed. Eng. 63 no. 9 (2016) 1887-1894.

[8] C. Chen, X. Guan, T. He, W. Yu, and B. Yang. Adaptive compressive engine for real-time electrocardiogram monitoring under unreliable wireless channels. IET Commun. 10 no. 6 (2016) 607–615.

[9] J. Yap, Y. Noh, and D. Jeong. The Deployment of Novel Techniques for Mobile ECG Monitoring. Int. J. Smart Home. 6 no. 4 (2012) 1–14.

[10] A. Arunan, R. K. Pathinarupothi, and M. V. Ramesh. A Real-time Detection and Warning of Cardiovascular Disease LAHB for a Wearable Wireless ECG Device. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI). (2016) 98–101.

[11] MIT-BIH. MIT-BIH ECG database. Physionet. (2016). Available on https: /www. physionet. org/physiobank/database.

[12] K. A. Alfarhan, Y. M. Mohd, and A. R. Mohdsaad. Automatic Arrhythmia Classification Method Using Simple Statistical Features. Int. J. Eng. Trends Technol. 44 no. 3 (2017) 107–111.

[13] M. Zavar, S. Rahati, M. -R. Akbarzadeh-T, and H. Ghasemifard. Evolutionary model selection in a wavelet-based support vector machine for automated seizure detection. Expert Syst. Appl. 38 no. 9 (2011) 10751–10758.

[14] J. W. Zheng, Z. B. Zhang, T. H. Wu, and Y. Zhang. A wearable mobihealth care system supporting real-time diagnosis and alarm. Med. Biol. Eng. Comput. 45 (2007) 877–885.

[15] F. Buendía-Fuentes, M. a. Arnau-Vives, a. Arnau-Vives, Y. Jiménez-Jiménez, J. Rueda-Soriano, E. Zorio-Grima, a. Osa-Sáez, L. V. Martínez-Dolz, L. Almenar-Bonet, and M. a. Palencia-Pérez. High-Bandpass Filters in Electrocardiography: Source of Error in the Interpretation of the ST Segment. ISRN Cardiol. 2012 (2012).

[16] B. S. Raghavendra, D. Bera, A. S. Bopardikar, and R. Narayanan. Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems. IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks. (2011).

[17] V. S. Chouhan and S. S. Mehta. Detection of QRS Complexes in 12-lead ECG using Adaptive Quantized Threshold. Int. J. Comput. Sci. Netw. Secur. 8 no. 1 (2008) 155–163.

[18] M. M. Tawfik, H. Selim, and T. Kamal. Human identification using time normalized QT signal and the QRS complex of the ECG. Communication Systems Networks and Digital Signal Processing (CSNDSP), 7th International Symposium. (2010) 755–759.

[19] E. Dolatabadi and S. Primak. Ubiquitous WBAN-based electrocardiogram monitoring system. IEEE 13th Int. Conf. e-Health Networking, Appl. Serv. Heal. (2011) 110–113.

[20] S. Xue, X. Chen, Z. Fang, and S. Xia. An ECG arrhythmia classification and heart rate variability analysis system based on android platform. 2nd Int. Symp. Futur. Inf. Commun. Technol. Ubiquitous Healthc. (2015) 1–5.

[21] L. She, Y. Song, S. Zhang, and Z. Xu. A precise ambulatory ECG arrhythmia intelligent analysis algorithm based on support vector machine classifiers. 3rd International Conference on Biomedical Engineering and Informatics, BMEI. (2010) 708–712.

[22] C. Chang and C. Lin. LIBSVM: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2 (2013) 1–39.

[23] R. Alcaraz and J. J. Rieta. Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms. Physiol. Meas. 29 no. 12 (2008) 1351–1369.

[24] L. Biel, O. Pettersson, L. Philipson, and P. Wide. ECG analysis: A new approach in human identification. IEEE Trans. Instrum. Meas. 50 no. 3 (2001) 808–812.

[25] K. Patel and R. Kher. Design and Implementation of Wireless Healthcare Monitoring System. Commun. Appl. Electron. 1 no. 7 (2015) 20–23.

[26] M. C. Paul, S. Sarkar, M. M. Rahman, S. M. Reza, and M. S. Kaiser. Low cost and portable patient monitoring system for e-Health services in Bangladesh. Int. Conf. Comput. Commun. Informatics. (2016) 1–4.

[27] M. Shen and S. Xue. Design and Implementation of Long-Term Single-Lead ECG Monitor. J. Biosci. Med. 3 (2015) 18–23.

[28] Raspberrypi organization. What is a Raspberry Pi. Raspberry pi organization. (2016) Available on https: /www. raspberrypi. org.

[29] Qt-company. Qt-Creator Integrated Development Environment. (2016). Available on https: /www. qt. io/ide.

[30] Datrend-Systems. ECG / Arrhythmia Simulator Operating Manual for Model 10A and 20A. Datrend Systems Inc. (2014).

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