# An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
## 9 Conclusions and Future Work  
In this paper, we have introduced the development of an innovative embedded system based on Raspberry Pi that revolutionizes the monitoring of electrocardiogram (ECG) signals in clinical applications. Our device offers a faster and more efficient approach to ECG monitoring, utilizing only three leads that can be easily connected to the human body. Once the leads are connected, and the device is powered on, the ECG signal is instantly displayed on the screen, enabling swift monitoring. To optimize the efficiency of our system, we have taken into consideration all factors that may affect signal accuracy. We have implemented the necessary filter stages in the circuit design, ensured a well-designed PCB layout, and developed a protective package to shield the circuit components from external noise sources. Through extensive testing using both ECG signals generated from a commercial Patient Simulator and real human subjects, our device has successfully measured ECG signals across various heart rates, accurately capturing all crucial peaks that serve as indicators of health conditions. The measured ECG results can be conveniently displayed on the graphical LCD screen and easily accessed on a smartphone or PC through the VNC viewer. Furthermore, the data can be securely stored on the cloud via a mobile application, allowing for seamless monitoring of patient conditions and providing valuable assistance to doctors in future diagnostics. In response to the rising demand for affordable and user-friendly medical diagnostic devices, our study focuses on the development of an embedded system that integrates the power of Raspberry Pi. This enables both wireless and wired interfaces, creating an ECG diagnostic embedded system capable of real-time detection and immediate response to any abnormalities in heart functionality. Our system effectively measures and displays the ECG signal by incorporating a comprehensive electronic circuit comprising analog and digital components. Within the analog section of the circuit, vital signal conditioning tasks, including amplification and noise filtering, are performed to ensure a clean signal within the desired frequency range. The entire system is powered using a power bank, enhancing its portability and accessibility. The digital segment incorporates an analog-to-digital converter, enabling the conversion of the received analog signal into a digital format compatible with Raspberry Pi. A graphical liquid-crystal display is employed to visualize the measured signal accurately. Through the successful measurement of ECG signals at various heart rates, our device captures all crucial peaks that serve as indicators of an individual's health condition. By comparing the signals obtained from healthy individuals with those exhibiting heart arrhythmias, valuable insights can be gained regarding their health status. The proposed system aims to be portable, cost-effective, and user-friendly in diverse environments, catering to the growing need for affordable and accessible medical diagnostic solutions. In conclusion, our embedded system based on Raspberry Pi offers a significant advancement in ECG monitoring, providing a faster, more efficient, and more comprehensive solution for healthcare professionals. The integration of cutting-edge technology, combined with rigorous testing and optimization, positions our device as a valuable tool in diagnosing and monitoring cardiac health conditions, ultimately contributing to improved patient care and well-being. In future work, we plan to employ the device for heart arrhythmia classification using a hybrid model developed in our previous research [37]. This model combines a 1D Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) to achieve rapid, accurate, and automated ECG beat-wise classification. This advancement will greatly aid doctors in diagnosing heart diseases in a more efficient manner.