Embedded DNN Classifier for Five Different Cardiac Diseases

The evolution of modern healthcare has been signif- icantly shaped by the convergence of connected sensors, smart Wearable Devices, Artificial Intelligence, and the Internet of Things giving rise to the domain of eHealth and offering invalu- able insights into the complications of heart health. eHealth’s im- pact extends to facilitating diagnosis, treatment, and medication for a diverse array of conditions, prominently including cardiac diseases. Despite substantial strides in medical technology, the detection of arrhythmia remains a persistent challenge, with early diagnosis holding the potential to avert numerous fatalities. This paper proposes an ultra-lightweight (876KB) Embedded- Deep Neural Network model specifically designed for resource- constrained devices. With high accuracy ranging from 94% to 99% for five classes identified from the MIT-BIH dataset, the proposed model is small enough to fit on tiny devices like the Arduino Nano BLE 33 Sense. This translates to low power consumption and real-time inference, making it ideal for screening cardiac diseases on wearable devices.