Personalized Panic Attack Detection: Leveraging Data from Wearables

چکیده

In the context of mental health management, timely and accurate detection of panic attacks is crucial for effective intervention. This paper presents a novel approach to personalize panic attack detection through the integration of data from wearable devices. By leveraging physiological signals such as heart rate, electrodermal activity, and accelerometer data, we propose a machine learning framework tailored to individual baseline and threshold variations.

Our methodology employs advanced data preprocessing techniques to handle noise and variability inherent in wearable data. We utilize a combination of feature extraction and selection methods to identify the most relevant physiological indicators of panic attacks. A personalized model is then constructed for each user, using supervised learning algorithms that adaptively learn from the user's historical data. This approach contrasts with traditional population-based models by accommodating personal physiological and behavioral nuances.

Experimental evaluations demonstrate the efficacy of our personalized models over conventional approaches, showing significant improvements in both sensitivity and specificity. Through cross-validation, our results indicate that the personalized models reduce false positives and enhance the accuracy of panic attack predictions, thereby providing a more reliable tool for users and healthcare providers. Additionally, the system's adaptability facilitates continuous learning, allowing it to refine predictions as more data becomes available.

Our findings underscore the potential of wearables in transforming mental health monitoring by providing real-time, personalized insights. This research contributes to the advancement of non-intrusive, scalable solutions for mental health care, and sets the stage for future developments in personalized health technology. The implications of this work extend beyond panic attack detection, suggesting broader applications in detecting other stress-related conditions through wearable technology.