LightAudioCNN: a novel deep neural network for audio-based parkinson’s disease recognition and subtype differentiation

Abstract This study introduces a Deep Neural Network architecture called LightAudioCNN. Its main purpose is to examine cord vibration patterns to improve the diagnosis of Parkinsons’ disease (PD) and differentiate it from similar conditions. LightAudioCNN represents a step in developing more objective and precise diagnostic tools, especially crucial in the early stages of PD, unlike the conventional symptom-based methods known for their arbitrary and unreliable nature. By analyzing vowel sounds (“a” and “i”) from a dataset of 83 participants, this study evaluates LightAudioCNN’s effectiveness while ensuring the reliability of its outcomes using a patient separation method. LightAudioCNN demonstrates high diagnostic accuracy and efficiency, achieving an Area Under the Curve (AUC) score of 0.99 in binary classification tasks and 0.96 in multiclass classification tasks with corresponding accuracy rates of 95% and 81%. These results were obtained through comparisons with Deep Neural Networks trained on Mel Spectrograms and contemporary transformer models processing Mel spectrograms or raw audio data. Additionally, the application of LightAudioCNN to the Italian Parkinson Speech dataset further substantiates its high diagnostic capability. On this dataset, LightAudioCNN achieved a mean accuracy of 97.69%, a precision of 97.88%, and an AUC score of 0.9873, illustrating its ability to capture complex speech patterns associated with Parkinson’s disease. The model’s performance was in line with the other deep learning models. Furthermore, the study highlights the versatility of LightAudioCNN beyond Parkinsons’ disease by proving its superiority in identifying COVID-19 by analyzing breath patterns and cough sounds. In this comparison, LightAudioCNN surpasses deep learning and traditional machine learning models by achieving a mean accuracy of 78.81% in the same scenarios. This proves the model’s potential for quickly and accurately diagnosing COVID-19, demonstrating its relevance across conditions. The model also has a small footprint of about 3.1 M parameters, which is about 7 times less than standard computer vision architectures such as ResNet50, allowing the integration of this technology locally into smartphone applications with the aim of managing and treating not just Parkinson’s’ Disease but also emerging health threats, like COVID-19.