Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India.
Department of Electronics and Communication Engineering, Indian Institute of Information Technology Design and Manufacturing, India.
Med Eng Phys. 2023 Sep;119:104028. doi: 10.1016/j.medengphy.2023.104028. Epub 2023 Jul 25.
Sleep is a natural state of rest for the body and mind. It is essential for a human's physical and mental health because it helps the body restore itself. Insomnia is a sleep disorder that causes difficulty falling asleep or staying asleep and can lead to several health problems. Conventional sleep monitoring and insomnia detection systems are expensive, laborious, and time-consuming. This is the first study that integrates an electrocardiogram (ECG) scalogram with a convolutional neural network (CNN) to develop a model for the accurate measurement of the quality of sleep in identifying insomnia. Continuous wavelet transform has been employed to convert 1-D time-domain ECG signals into 2-D scalograms. Obtained scalograms are fed to AlexNet, MobileNetV2, VGG16, and newly developed CNN for automated detection of insomnia. The proposed INSOMNet system is validated on the cyclic alternating pattern (CAP) and sleep disorder research center (SDRC) datasets. Six performance measures, accuracy (ACC), false omission rate (FOR), sensitivity (SEN), false discovery rate (FDR), specificity (SPE), and threat score (TS), have been calculated to evaluate the developed model. Our developed system attained the classifications ACC of 98.91%, 98.68%, FOR of 1.5, 0.66, SEN of 98.94%, 99.31%, FDR of 0.80, 2.00, SPE of 98.87%, 98.08%, and TS 0.98, 0.97 on CAP and SDRC datasets, respectively. The developed model is less complex and more accurate than transfer-learning networks. The prototype is ready to be tested with a huge dataset from diverse centers.
睡眠是身体和大脑的自然休息状态。它对人类的身心健康至关重要，因为它有助于身体自我恢复。失眠是一种睡眠障碍，会导致入睡困难或保持睡眠困难，并可能导致多种健康问题。传统的睡眠监测和失眠检测系统既昂贵又费力且耗时。这是第一项将心电图（ECG）频谱图与卷积神经网络（CNN）集成在一起的研究，旨在开发一种模型，用于通过识别失眠来准确测量睡眠质量。连续小波变换已被用于将一维时域 ECG 信号转换为二维频谱图。获得的频谱图被馈送到 AlexNet、MobileNetV2、VGG16 和新开发的 CNN 中，以实现自动化的失眠检测。在所提出的 INSOMNet 系统上对循环交替模式（CAP）和睡眠障碍研究中心（SDRC）数据集进行了验证。使用准确度（ACC）、假遗漏率（FOR）、敏感度（SEN）、假发现率（FDR）、特异性（SPE）和威胁评分（TS）这 6 个性能指标来评估所开发的模型。我们开发的系统在 CAP 和 SDRC 数据集上分别达到了 98.91%、98.68%、1.5%、0.66%、98.94%、99.31%、0.80%、2.00%、98.87%、98.08%和 0.98%、0.97%的分类准确度。与转移学习网络相比，所开发的模型更简单、更准确。该原型已准备好使用来自不同中心的大量数据集进行测试。
