Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 onemin, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems. Continuous monitoring of respiration plays a key role in the detection and management of different conditions, such as stress 1,2 and sleep disorders 3,4. Biomarkers like respiratory rate, breathing phases, and tidal volume are relevant for the detection of mental stress 1 , anxiety 2 , and sleep apnea events 5,6. In addition, the coupling between respiration and heart rate has been used as a biomarker for the aforementioned conditions 7,8 as well as for the understanding of the interactions between the cardiac and respiratory systems 9. Despite the importance of monitoring respiration, its recording requires the use of invasive and intrusive sensors like thermistors, spirometers, and respiratory belts. Even though these sensors are regularly used, for instance during polysomnographic recordings, their use in ambulatory systems is very limited since they not only interfere with natural breathing, but are often associated with high costs and low comfort. Different studies have shown that the respiratory rate, and even the respiratory wave morphology, can be approximated by ECG-derived respiration (EDR) 5,10-20. The derived signal is defined by certain morphological properties of the ECG particularly influenced by respiration. This influence can be explained by the respiratory-induced chest movements that cause changes in the position of the electrodes relative to the cardiac vector 21. Moreover, the filling and emptying of the lungs cause changes in the electrical impedance of the chest. As a result, the morphology of the ECG is modulated by respiration.

In the present study, a photoplethysmographic (PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder (MDD) and healthy control (HC) subjects is presented. Methods: PPG recordings of 40 MDD and 40 HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate (HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity. Results: Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave (A13) in both HC and MDD group. However, autonomic reactivity indices related to A13 reached higher values in HC than in MDD subjects (Cohen's d = −0.81, AUC = 0.74), implying that the stress response in depressed patients is reduced. A statistically significant (p < 0.001) negative correlation (r = −0.5) between depression severity scores and A13 was found. Conclusion: A decreased autonomic reactivity is associated with higher degree of depression. Significance: Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression.

The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated. Methods: The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal-and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals. Results: Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015±0.021 Hz and 0.019±0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034±0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA. Conclusion: The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed. Significance: The respiratory rate can be robustly estimated from the ECG in the presence of AF.

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of , a sensitivity of and a specificity of , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.

The proposed method can help to assess alternations of nonlinear cardiorespiratory interactions related to ANS dysfunction and physiological regulation of HRV in cardiovascular diseases.

Stress is a healthy natural response to a perceived or actual threat. However, when stress is persistent, it may decrease work productivity, increase the risk of diseases, and affect the quality of life. Stress is reflected in physiological variables, such as heart rate, blood pressure, and pulse wave velocity among others. A photoplethysmogram (PPG) contains information related to pulse rate and blood pressure. This study analyses parameters derived from PPG signal morphology for mental stress assessment.A low-complexity algorithm is designed using bandpass filtered higher-order derivatives of the PPG signal for estimation of six morphological parameters: the forward pulse wave amplitude A 1 , the systole and diastole durations T 1 and T d , the time delays of reflected waves T 12 and T 13 from the renal and iliac sites in the central arteries, and the pulse duration T p . The parameters were investigated on a set of 18 healthy subjects by using a modified Trier Social Stress Test.The results show that the most sensitive PPG morphology parameters to mental stress are the amplitude of forward wave A 1 , the duration of diastole T d , the time delay of the reflected wave T 13 , and the pulse-to-pulse interval T p .

In this work quadratic phase coupling between respiration and heart rate variability (HRV) has been studied during emotional and mental stress using wavelet crossbispectrum (WCB). A total of 80 healthy volunteers subjected to a standard stress protocol have been analyzed. Some features derived from the WCB, such as the frequencies at which the maximum peak is located, the distribution of the dominant peaks and the phase entropy have shown statistical significant differences between stress and relax stages. A support vector machine classifier based on these features discriminates stress stages from relax ones with an accuracy ranging from 68 to 89%, suggesting that the interactions between respiration and HRV are altered during stress and may be used to assess it.

In this paper a method for the assessment of Quadratic Phase Coupling (QPC) between respiration and Heart Rate Variability is presented and applied to study cardiorespiratory couplings during a tilt table test. Strong QPC related to the dominant respiratory frequency is present and remains unchanged during Autonomic Nervous System changes.