IoT Based Heart Attack Early Prediction

Abstract

Heart attack is most common disease that Engulfs the patients precious life. This disease attacks a man so in a flash that it barely gets treated. Many systems that detect heart attack came into the picture but they do have many flaws .Some system does not work properly in cold weather and other doesn’t give accuracy . To have a solution for Heart attack utilization of IoT and sensor system could be beneficial. Heart Attack Early Prediction System predicts the Heart rate and accordingly sends the notification to the patients doctor and acquaintances.

Keywords: Heart Attack, IoT (Internet Of Things), Health Parameters, Heart Disease Prediction, Oximeter, Heart rate sensor, Android App.

Introduction

Heart attack is most frequently occurring disease that has a consequence of loosing a precious life. Numerous people are mislaying their life owing to heart attack and shortage of medical attention to patient at correct stage. There are many solutions that detects the heart beat like Fit Bit ,Garmin and many more. Fit Bit and Garmin works like a smart watch. These devices track the users heart rate .Fit Bit uses build in or connected GPS for many distance and tracking features. The device are categorized based on fitness level : Everyday, Active and Performance.

Distributed computing helps the patient for monitoring the Health status through albeit cell phones. Their is another Device that is a laser device that monitors the Heart Beat, Breathing rate and muscle activity from up to a meter

away with a single sensor called Zeev.Multi Detecting gadgets also came into the picture that Monitors the Heart and even status logging and social Sharing of recorded data.

To overcome this health issue, an Android application could be designed and implemented. The patient will be having the hardware device with android application. The Heartbeat sensor will allow checking heartbeat reading and oxygen level and transmit them through Wi-Fi module.

Heart Attack Early Prediction System will sense the Heart rate and oxygen level of the patient and will display on the Android Application. If the Heart rate extends the threshold value .Then the patient will be considered in danger situation.

What is IoT

Internet of things simply “A network of internet connected objects able to collect and exchange data”. It is commonly abbreviated as IOT.

IOT is the network of devices such as vehicles and home appliances that contain electronics, software, sensors, actuators, and connectivity that allows these things to connect, interact and exchange data. Embedded with the technology, these devices can communicate and interact through internet, and they can be remotely monitored and controlled

Sensor Technology

The capacity to recognize changes in the physical status of things is additionally fundamental for chronicle changes in the earth. In such manner, sensors assume an urgent job in overcoming any issues between the physical and virtual universes, and empowering things to react to changes in their physical condition. Sensors gather information from their condition, producing data and bringing issues to light about setting. For instance, sensors in an electronic coat can gather data about changes in outer temperature and the parameters of the coat can be balanced in like manner.

Review of Literature

Milan Kumari et al. fathom cardiovascular disease dataset utilizing diverse information mining calculations, for example, Support Vector Machine, Artificial neural systems (ANNs), Decision Tree, and RIPPER classifier. The creators break down the execution of these calculations through a few factual investigation factors, for example, affectability, specificity, exactness, mistake rate. A choice emotionally supportive network for conclusion of Congenital Heart. Using data mining and artificial neural networks, the system predicts Heart disease with nearly 100% accuracy. [1]

Niti Guru et al. connected a neural system for forecast of heart disease, circulatory strain and sugar. A model Intelligent Heart Disease Prediction System (IHDPS) in view of information mining procedures is introduced by Sellappan Palaniappan et al. The models are trained and validated against a test dataset. Classification Matrix Methods are used to evaluate the effectiveness of the models. Neural Network is Difficult to understand. The system has 89.9 percent accuracy according to the Research.

Distributed computing has been incorporated in a health care framework. The creators have introduced a cloud-based keen healthcare monitoring framework (CIHMS) for giving restorative input to a patient through the cloud. Albeit cell phones are constantly viewed as a promising instrument to screen and deal with the patient's own health status, these gadgets have some innate restrictions in calculation or information concentrated undertakings. ECG monitoring system

Contains WI-FI Bluetooth and Zigbee. ECG signs were gathered and were transmitted to the IOT cloud utilizing WI-FI. This information were send to the family of the patient. Remote health care framework for monitoring electrocardiographic and temperature information has been introduced in [5]. The framework comprises of three modules, in particular, an equipment module, Bluetooth module, and show module. The equipment module is utilized for information securing. The Bluetooth module is utilized for information transmission. At long last, the information is shown by utilizing the presentation module. The obtained clinical information is sent to a database server by utilizing GPRS or Wi-Fi. The exhibitions of the framework have been tried on various patients and it has been discovered that the introduced framework is extremely useful for the doctors.

A walking framework for monitoring the physical restoration patients has been accounted for in [6]. The framework comprises of a multi-sensor based monitoring gadget, a cell phone with customer application, an administration situated engineering based server arrangement, and an application. The framework has been tried in a controlled condition comprising of some healthy volunteers and some congestive heart disappointment patients. The test outcomes demonstrate that the introduced framework can identify and screen congestive heart disappointment and it can send criticism to the attendants for patient development.

Ongoing universal health care framework for monitoring ECG motions by utilizing cell phone has been exhibited in [7]. By utilizing this framework the client can screen his ECG flag. The creators have introduced a calculation for unusual heartbeat detection and irregular heartbeat check delineate). The execution of the introduced framework has been assessed against the MIT-BIH typical arrhythmia database. It has been accounted for that the framework can distinguish at a top with a win rate of 97.8 percent and it is additionally ready to recognize strange heartbeat condition with a win rate of 78.9 percent. An unavoidable healthcare framework empowering self-administration for constant The introduced framework in [8] comprises of patient health monitoring framework, status logging, and social sharing of the recorded data. The framework has been executed by a cell phone, a wearable multi-detecting gadget contains pulse sensor and temperature sensor, an administration situated engineering for correspondence, and smaller scale blogging administrations. The framework has been tried on 16 patients. The test outcomes demonstrate that the introduced framework is anything but difficult to learn and advantageous to use by endless patients.

Heart Attack Early Prediction System could contain Arduino board and a Wi-Fi module. The Heart Beat sensor can begin detecting heart rate readings. If the Heart beat will be above 120 bpm then the patient will be in the danger situation. If the oxygen level will be below 80 bpm then again the patient will be considered in the dangerous situation. Likewise, with the utilization of Wi-Fi module it will transmit the information over web. The framework will begin monitoring the heart rate of patient and instantly the heart rate goes above or beneath as far as possible the framework will send notification in the Android Application. An android application will track the heartbeat of specific patient and screen it effectively and give the crisis message on odds of Heart Attack.

Conclusion

Heart disease is one of the major cause that leads to Death and early prediction of heart disease is very important for everyone. Garmin is a smart watch and it predicts the heart attack but it does not work accurately in cold weather. Fit Bit is another alternative but even it has many flaws i.e. while the tracker is charging, patient can not move and it is expensive too. Heart Attack Early Prediction System has many pros Over the existing solutions. It takes two inputs i.e. Pulse rate and oxygen level of the patient to have the accuracy.It transmits the Notifications to the patients doctor and Acquaintances.

References

1. Milan Kumari, Sunila Godara, Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, IJCST Vol. 2, Issue 2, June 2011.
2. Niti Guru, Anil Dahiya, Navin Rajpal, Decision Support System for Heart Disease Diagnosis Using Neural Network, Delhi Business Review, Vol. 8, No. 1, January-June 2007.
3. Sellappan Palaniappan, Rafiah Awang, Intelligent Heart Disease Prediction System Using Data Mining Technique, 978-1-4244- 1968-5/08/25.00 2008 IEEE.
4. http://www.nlm.nih.gov/medlineplus/magazine/issues/winter11
5. Bourouis, A., Feham, M., and Bouchachia, A.(2011), Ubiquitous Mobile Health Monitoring System for Elderly (UMHMSE), International Journal of Computer Science and Information Technology, Vol.2, No. 3, June, pp. 74-82
6. Yuce, M. R.(2010) Implementation of wireless body area networks for healthcare systems, Sensor and Actuators A: Physical, Vol. 162, No. 1, July, pp. 116-129
7. Lei Clifton, David A. Clifton, Marco A. F. Pimentel, Peter J. Watkinson, and Lionel Tarassenko (2014), Predictive Monitoring of Mobile Patients by Combining Clinical Observations with Data From Wearable Sensors, IEEE Journal of Biomedical and Health Informatics, Vol. 18, No. 3, May, pp. 722-730
8. Parane, K.A., Patil, N.C. ; Poojara, S.R.; Kamble, T.S(2014) Cloud based Intelligent Healthcare Monitoring System, In the proceedings of International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), February 7-8, Ghaziabad, Indian, pp. 697-701