Development of the ‘Healthcor’ System as a Cardiac Disorders Symptoms Detector using an Expert System based on Arduino Uno

In the modern era, our lifestyles are very fast-moving; this makes us highly susceptible to diseases, especially those associated with heart problems. In this research, we developed a portable early detection system for cardiac disorders. This system consists of passive electrodes, named SHIELD-EKG-EMG-PA; a shield which allows Arduino-like boards to capture electrocardiography (ECG) and electromyography (EMG) signals, named SHIELD-EKG-EMG, both devices produced by Olimex; a microcontroller, based on Arduino Uno; and an expert system which is implemented by a personal computer. This system detects time intervals of various segments in ECG signals which are captured by the devices; it then analyzes the signals in order to determine whether the patient has cardiac disorders. We call our detecting system the HEALTHCOR system. A database was established, containing various possible values of parameters in ECG signals. The types of diseases that can be detected are heart rhythm disorders including sinus bradycardia, sinus tachycardia, sinus arrhythmia, and cardiac symptoms associated with intervals and the wave height, such as myocardial infarction. From our tests, the accuracy of our system is 96%. The resultant diagnoses of four patients are all appropriate, and used a commercial 12-lead electrocardiograph.

Cardiac disorders detection system, Expert system, Electrocardiograph, HEALTHCOR

Acharya, R., Bhat, P.S., Iyengar, S.S., Roo, A., Dua, S., 2002. Classification of Heart Rate Data using Artificial Neural Network and Fuzzy Equivalence Relation. The Journal of the Pattern Recognition Society, Volume 130, pp. 101–108

Afonso, V.X., in Tompkins, W.J. (Ed.), 2000. Biomedical Digital Signal Processing: C-Language Examples and Laboratory Experiments for the IBM® PC. Prentice-Hall, Inc., New Jersey, pp. 236–264

Alexakis, C., Nyongesa, H.O., Saatchi, R., Harris, N.D., Davis, C., Emery, C., Ireland, R.H., Heller, S.R., 2003. Feature Extraction and Classification of Electrocardiogram (ECG) Signals Related to Hypoglycemia. Proceedings of Computers in Cardiology. Volume 30, pp. 537–540

Ameneiro, S.B., Fernandez-Delgado, M., Vila-Sobrino, J.A., Regueiro, C.V., Sanchez, E., 1998. Classifying Multichannel ECG Patterns with an Adaptive Neural Network. IEEE Engineering in Medicine and Biology, Volume 17, pp. 45–55

Andreao, R.V., Dorizzi, B., Boudy, J., 2006. ECG Signal Analysis through Hidden Markov Models. IEEE Transactions on Biomedical Engineering, Volume 53, pp. 1541–1549

Christov, I.I., 2004. Real Time Electrocardiogram QRS Detection using Combined Adaptive Threshold. BioMedical Engineering Online, Volume 3(28), pp. 1–9

Coast, D.A., Stern, R.M., Cano, G.G., Briller, S.A., 1990. An Approach to Cardiac Arrhythmia Analysis using Hidden Markov Models. IEEE Transactions on Biomedical Engineering, Volume 37, pp. 826–836

de Chazal, P., O’Dwyer, M., Reilly, R.B., 2000. A Comparison of the ECG Classification Performance of Different Feature Sets. IEEE Transactions on Biomedical Engineering, Volume 27, pp. 327–330

de Chazal, P., O’Dwyer, M., Reilly, R.B., 2004. Automatic Classification of Heartbeats using ECG Morphology and Heartbeat Interval Features. IEEE Transactions on Biomedical Engineering, Volume 51, pp. 1196¬–1206

Fernández-Delgado M., Ameneiro, S.B., 1998. MART: A Multichannel ART-based Neural Network. IEEE Transactions on Neural Network, Volume 9, pp. 139–150

Hu, Y.H., Tompkins, W.J., Urrusti, J.L., Afonso, V.X., 1994. Applications of Artificial Neural Networks for ECG Signal Detection and Classification. The Journal of Electrocardiology, Volume 26, pp. 66–73

Hu, Y., Palreddy, S., Tompkins W.J., 1997. A Patient-adaptable ECG Beat Classifier using a Mixture of Experts Approach. IEEE Transactions on Biomedical Engineering, Volume 44, pp. 891–900

Ivanov, P., Ma, Q.D.Y., Bartsch, R.P., Hausdorff, J. M., Amaral, L.A.N., Schulte-Frohlinde, V., Stanley, H.E., Yoneyama, M., 2009. Levels of Complexity in Scale-invariant Neural Signals. Physical Review, Volume 79(4), pp. 041920–1–12

Köhler, B.-U., Hennig, C., Orglmeister, R., 2002. The Principles of Software QRS Detection. IEEE Engineering in Medicine and Biology Magazine, Volume 21, pp. 42–57

Köhler, B.-U., Hennig, C., Orglmeister, R., 2003. QRS Detection using Zero Crossing Counts. Progress in Biomedical Research, Volume 8(3), pp. 138–145

Lagerholm, M., Peterson, C., Braccini, G., Edenbrandt, L., Sörnmo, L., 2000. Clustering ECG Complexes using Hermite Functions and Self-organizing Maps. IEEE Transactions on Biomedical Engineering, Volume 47, pp. 838–848

Linh, T.H., Osowski, S., Stodolski, M., 2003. On-line Heartbeat Recognition using Hermite Polynomials and Neuro-fuzzy Network. IEEE Transactions on Instrumentation and Measurement, Volume 52, pp. 1224–1231

Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M., Babyn, P., Alirezaie,

J., 2010. A Fast Expert System for Electrocardiogram Arrhythmia Detection. Expert System, Volume 27, pp. 180–200

Minami, K., Nakajima, H., Toyoshima, T., 1999. Real-time Discrimination of Ventricular Tachyarrhythmia with Fourier-Transform Neural Network. IEEE Transactions on Biomedical Engineering, Volume 46, pp. 179–185

Osowski, S., Linh, T.H., 2001. ECG Beat Recognition using Fuzzy Hybrid Neural Network. IEEE Transactions on Biomedical Engineering, Volume 48, pp. 1265–1271

Osowski, S., Hoai, L.T., Markiewicz, T., 2004. Support Vector Machine-based Expert System for Reliable Heartbeat Recognition. IEEE Transactions on Biomedical Engineering, Volume 51(4), pp. 582–589

Pan, J., Tompkins, W.J., 1985. A Real-time QRS Detection Algorithm. IEEE Transactions on Biomedical Engineering, Volume 32(3), pp. 230–236

Piotrowskia, Z., Rozanowski, K., 2010. Robust Algorithm for Heart Rate (HR)

Detection and Heart Rate Variability (HRV) Estimation. ACTA PHYSICA POLONICA, Volume 118(1), pp. 131–135

Romero, I., Serrano, L., 2001. ECG Frequency Domain Features Extraction: A New Characteristic for Arrhythmias Classification. In: Proceedings of the 23rd Annual International Conference on Engineering in Medicine and Biology Society, pp. 2006–2008

Sarkaleh, M.K., Shahbahrami, A., 2012. Classification of ECG Arrhythmias using Discrete Wavelet Transform and Neural Networks. International Journal of Computer Science, Engineering and Applications, Volume 2(1), pp. 1–13

Shyu, L., Hu, W., 2008. Intelligent Hybrid Methods for ECG Classification: A Review. Journal of Medical and Biological Engineering, Volume 28, pp. 1–10

Surda, J., Lovas, S., Pucik, J., Jus, M., 2007. Spectral Properties of ECG Signal. In: Proceedings of the 17th International Conference on Radioelektronika 2007, Brno, 24th - 25th April, Czech Republic, pp. 1–5