Respiratory Event Detection During Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data.

This paper presents an automatic algorithm for the detection of respiratory events in patients using electrocardiogram (ECG) and respiratory signals. The proposed method was developed using data of polysomnogram (PSG) and those recorded from a patch-type device. In total, data of 1,285 subjects were used for algorithm development and evaluation. The proposed method involved respiratory event detection and apnea-hypopnea index (AHI) estimation. Handcrafted features from the ECG and respiratory signals were applied to machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, random forest, multi-layer perceptron, and the support vector machine (SVM). High performance was demonstrated when using SVM, where the overall accuracy achieved was 83% and the Cohen's kappa was 0.53 for the minute-by-minute respiratory event detection. The correlation coefficient between the reference AHI obtained using the PSG and estimated AHI as per the proposed method was 0.87. Furthermore, patient classification based on an AHI cutoff of 15 showed an accuracy of 87% and a Cohen's kappa of 0.72. The proposed method increases performance result, as it records the ECG and respiratory signals simultaneously. Overall, it can be used to lower the development cost of commercial software owing to the use of open datasets.

Robust Method for Screening Sleep Apnea With Single-Lead ECG Using Deep Residual Network: Evaluation With Open Database and Patch-Type Wearable Device Data.

This paper proposes a robust method to screen patients with sleep apnea syndrome (SAS) using a single-lead electrocardiogram (ECG). This method consists of minute-by-minute abnormal breathing detection and apnea-hypopnea index (AHI) estimation. Heartbeat interval and ECG-derived respiration (EDR) are calculated using the single-lead ECG and used to train the models, including ResNet18, ResNet34, and ResNet50. The proposed method, using data from 1232 subjects, was developed with two open datasets and experimental data and evaluated using two additional open datasets and data acquired from an abdomen-attached wearable device (in total, data from 189 subjects). ResNet18 showed the best results, having an average Cohen's kappa coefficient of 0.57, in the abnormal breathing detection. Moreover, SAS patient classification, with 15 as the AHI threshold, yielded an average Cohen's kappa coefficient of 0.71. The results of patient classification were biased toward data from the wearable patch-type device, which may be influenced by different ECG waveforms. The proposed method is tuned with a sample of the data from the device, and the performance result of Cohen's kappa increased from 0.54 to 0.91 for SAS patient classification. Our method, proposed in this paper, achieved equivalent performance results with data recorded using an abdomen-attached wearable device and two open datasets used in previous studies, although the method had not used those data during model training. The proposed method could reduce the development costs of commercial software, as it was developed using open datasets, has robust performance throughout all datasets.

Heart Rate Variability in Postoperative Patients with Nonfunctioning Pituitary Adenoma.

BACKGROUND: Decreased heart rate variability (HRV) has been reported to be associated with cardiac autonomic dysfunction. Hypopituitarism in nonfunctioning pituitary adenoma (NFPA) is often linked to increased cardiovascular mortality. We therefore hypothesized that postoperative NFPA patients with hormone deficiency have an elevated risk of HRV alterations indicating cardiac autonomic dysfunction. METHODS: A total of 22 patients with NFPA were enrolled in the study. Between 3 and 6 months after surgery, a combined pituitary function test (CPFT) was performed, and HRV was measured. The period of sleep before the CPFT was deemed the most stable period, and the hypoglycemic period that occurred during the CPFT was defined as the most unstable period. Changes in HRV parameters in stable and unstable periods were observed and compared depending on the status of hormone deficiencies. RESULTS: In patients with adrenocorticotropic hormone (ACTH) deficiency with other pituitary hormone deficiencies, the low frequency to high frequency ratio, which represents overall autonomic function and is increased in the disease state, was higher (P=0.005). Additionally, the standard deviation of the normal-to-normal interval, which decreases in the autonomic dysfunction state, was lower (P=0.030) during the hypoglycemic period. In panhypopituitarism, the low frequency to high frequency ratio during the hypoglycemic period was increased (P=0.007). CONCLUSION: HRV analysis during CPFT enables estimation of cardiac autonomic dysfunction in patients with NFPA who develop ACTH deficiency with other pituitary hormone deficiencies or panhypopituitarism after surgery. These patients may require a preemptive assessment of cardiovascular risk.