Sleep apnea (SA) is a significant respiratory condition that poses a major
global health challenge. Previous studies have investigated several machine and
deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite
these advancements, conventional feature extractions derived from ECG signals,
such as R-peaks and RR intervals, may fail to capture crucial information
encompassed within the complete PQRST segments. In this study, we propose an
innovative approach to address this diagnostic gap by delving deeper into the
comprehensive segments of the ECG signal. The proposed methodology draws
inspiration from Matrix Profile algorithms, which generate an Euclidean
distance profile from fixed-length signal subsequences. From this, we derived
the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean
Distance Profile (MeanDP) based on the minimum, maximum, and mean of the
profile distances, respectively. To validate the effectiveness of our approach,
we use the modified LeNet-5 architecture as the primary CNN model, along with
two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification
tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset
revealed that with the new feature extraction method, we achieved a per-segment
accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it
yielded the highest correlation compared to state-of-the-art methods, with a
correlation coefficient of 0.989. By introducing a new feature extraction
method based on distance relationships, we enhanced the performance of certain
lightweight models, showing potential for home sleep apnea test (HSAT) and SA
detection in IoT devices. The source code for this work is made publicly
available in GitHub: this https URL

使用基于距离关系的新特征提取方法，提高了轻量级模型的性能，为家庭睡眠呼吸暂停监测和睡眠呼吸暂停检测在物联网设备中的应用潜力。

MPCNN：一种基于矩阵剖面的卷积神经网络睡眠呼吸暂停分级方法

MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea  Classification

This study reveals a possible correlation between splashing morphology and
the normalized impact force exerted by an impacting drop on a solid surface.
This finding is obtained from a newly proposed feature extraction method and a
subsequent interpretation of the classification of splashing and non-splashing
drops performed by an explainable artificial intelligence (XAI) video
classifier. Notably, the values of the weight matrix elements of the XAI that
correspond to the extracted features are found to change with the temporal
evolution of the drop morphology. We compute the rate of change of the
contributions of each frame with respect to the classification value of a video
as an important index to quantify the contributions of the extracted splashing
and non-splashing features at different impact times to the classification of
the XAI model. Remarkably, the rate computed for the extracted splashing
features is found to closely match the profile of the normalized impact force,
where the splashing features are most pronounced immediately after the
normalized impact force reaches its peak value. This study has provided an
example that clarifies the relationship between the complex morphological
evolution of a splashing drop and physical parameters by interpreting the
classification of an XAI video classifier.

通过解释性人工智能（XAI）视频分类器对飞溅和非飞溅滴体进行分类并计算每帧对于分类值的贡献变化率的方法，本研究揭示了飞溅形态与滴体对固体表面冲击力之间的可能相关性，进而提供了飞溅滴体复杂形态演化与物理参数之间关系的示例。

通过可解释的人工智能解析，揭示飞溅液滴形态演化与施加冲击力之间的相关性

Correlation between morphological evolution of splashing drop and  exerted impact force revealed by interpretation of explainable artificial  intelligence