Fuzzy expert based real time monitoring system for patients with chronic heart failure through IOT
Muriuki, Isaack Mwenda
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Data from the World Health Organization has placed CHF as the number one global killer. It remains the only cardiovascular disease with an increasing hospitalization burden and a continuous drain on health care budgets. Heart failure is a complex clinical syndrome of symptoms that suggest the heart is unable to pump blood efficiently as it should. Heart failure signs and symptoms may include irregular heart rate, blood pressure, fatigue and weakness. The hard reality, with which doctors contend every day, is that the effects of these conditions often manifest too gradually for people to recognize. It falls to the healthcare system to deal with these diseases after they’ve advanced to a serious stage, often at a great financial cost. Effective therapy and treatment in CHF patients require thorough continuous monitoring of patients vitals. Doctors require information on patients; blood pressure, heart electrocardiography activities heart rate and temperature to predict the heart failure attacks and respond swiftly. The typical way to diagnose and monitor CHF patients is by use of bedside patient monitoring systems which requires monitoring within the confines of the hospital. Such monitoring equipment are available in very few hospitals in Kenya and that is an impediment to proper therapy and treatment for CHF patients. The challenges faced in using the existing methods include; lack of flexibility for the patient as there is need for long term monitoring in a hospital setup, financial burden on the patients when they are hospitalized, obtrusive nature of the current monitoring systems making it not suitable for monitoring outdoors. This research applies scrum methodology to design, develop and test a fuzzy based expert system for real time monitoring of chronic heart failure patients through IoT. The IoT architecture contains sensors to capture heart rate, heartbeat, and temperature values from the patients and transmit values from the Arduino board to an IoT server via a GSM communication module. A mobile application will be developed to enable the care givers to monitor the patient remotely. The recommended vital parameters will be keyed to the system to enable it detect anomalies. As a result, patient's doctor and care-givers can see CHF patients vital current health conditions in real-time and get sms alerts in case of anomalies’ to enable them respond swiftly. This model reduce re-hospitalization, enables adjustment of therapy to accommodate change in the patient’s condition and reduces death rates caused by CHF.