# Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

## TL;DR

This paper introduces a deep learning model for automatic detection of sleep-related events like arousals and leg movements, demonstrating promising results and potential for broader sleep analysis applications.

## Contribution

A novel deep learning approach incorporating RNNs for detecting sleep events, with optimized configurations for arousal and leg movement detection.

## Key findings

- Arousal detection achieved F1 score of 0.75 with RNN and dynamic window.
- Leg movement detection achieved F1 score of 0.65 with static window.
- Model shows potential for general sleep analysis, with room for improvement.

## Abstract

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model.