An Efficient and Affordable R-Pi Based Cardiac Disease Detection System Neha Arora and Biswajit Mishra Abstract This paper proposes a Raspberry Pi (R-Pi) based system to automatically detect and classify most of the atrial and ventricular cardiac diseases. The system provides a necessary solution for resource constrained regions to timely detect fatal cardiac conditions. The R-Pi receives the user specific information from the ECG application specifically developed for the smart mobile phones and 2-Lead ECG data from the ECG sensors connected with limb electrodes and the Arduino Nano board. The ECG data along with the user specific information is further processed for lead separation, ECG feature extraction, and disease classification. The obtained results are sent to the doctor via email. Along with the system development, this work proposes various algorithms for feature points detection and disease classifications. Keywords ECG · R-Pi · Disease classification · Feature extraction 1 Introduction ECG recordings are frequently used to detect Arrhythmias that are relatively quiet in the early stages. However, they may provide valuable knowledge about an indi- vidual’s fitness and aid in the detection of underlying heart anomalies and the timely detection can prove to be life saving. While not all of arrhythmias are permanent or necessitate medical treatment, they can signal the onset of serious heart diseases. An ECG depicts the electrical activity of the heart and provides a large amount of information on the functionality of the heart required for the proper diagnosis of Supported by DST/SERB- CRG, Govt. of India, Research fund Ref: CRG/2019/004747. N. Arora · B. Mishra (B ) Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar, Gujarat, India e-mail: biswajit_mishra@daiict.ac.in N. Arora e-mail: 201721007@daiict.ac.in © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 B. Mishra et al. (eds.), Artificial Intelligence Driven Circuits and Systems, Lecture Notes in Electrical Engineering 811, https://doi.org/10.1007/978-981-16-6940-8_1 1