An intelligent biosensing platform using phase space reconstruction-assisted convolutional neural network for drug-induced cardiotoxicity assessment

Drug-induced cardiotoxicity often leads to patient deaths and drug recalls. Interdigitated electrode (IDE)-based cellular impedance detection instrument has been integrated with intelligent algorithms to screen cardiotoxicity based on cardiomyocytes. These intelligent biosensing systems generally employ traditional machine learning methods to assess drug-induced cardiotoxicity by analyzing cardiomyocytes mechanical beating signals. However, modern deep learning methods with robustness and flexibility have not been integrated in IDE-based platform to screen cardiotoxicity. Here, for the first time, we implemented deep convolutional neural network (CNN) to analyze cardiomyocytes mechanical beating signals for cardiotoxicity assessment. This method can eliminate the feature engineering procedures, such as manual design and extraction of signal features required by traditional machine learning methods. To facilitate two-dimensional (2-D) CNN analysis, we utilized phase space reconstruction to convert one-dimensional beating signals into 2-D image representations as well as take into account nonlinear dynamic information. The phase space reconstruction-assisted convolutional neural network (PSRCNN) is capable of accurately categorizing drug-induced cardiotoxicities and predicting cardiotoxicity levels. It obtains accuracies ranging from 0.82 to 0.99 when recognizing the cardiotoxicity of newly developed drugs, which are represented by drugs that are not used during model training. Furthermore, we explore in depth to compare the performance of CNN with other traditional machine learning methods. The PSRCNN method can achieve higher accuracies when compared to other methods. The PSRCNN-based biosensing platform is highly potential in improving the efficiency and accuracy of high-throughput screening of newly developed drugs for cardiotoxicity during drug discovery.