Obstructive sleep apnea event detection using explainable deep learning models for a portable monitor.

Background: Polysomnography (PSG) is the gold standard for detecting obstructive sleep apnea (OSA). However, this technique has many disadvantages when using it outside the hospital or for daily use. Portable monitors (PMs) aim to streamline the OSA detection process through deep learning (DL). Materials and methods: We studied how to detect OSA events and calculate the apnea-hypopnea index (AHI) by using deep learning models that aim to be implemented on PMs. Several deep learning models are presented after being trained on polysomnography data from the National Sleep Research Resource (NSRR) repository. The best hyperparameters for the DL architecture are presented. In addition, emphasis is focused on model explainability techniques, concretely on Gradient-weighted Class Activation Mapping (Grad-CAM). Results: The results for the best DL model are presented and analyzed. The interpretability of the DL model is also analyzed by studying the regions of the signals that are most relevant for the model to make the decision. The model that yields the best result is a one-dimensional convolutional neural network (1D-CNN) with 84.3% accuracy. Conclusion: The use of PMs using machine learning techniques for detecting OSA events still has a long way to go. However, our method for developing explainable DL models demonstrates that PMs appear to be a promising alternative to PSG in the future for the detection of obstructive apnea events and the automatic calculation of AHI.

Assessing the Feasibility of Replacing Subjective Questionnaire-Based Sleep Measurement with an Objective Approach Using a Smartwatch.

In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a "similarity level" parameter increases the adaptability and reusability of study findings in individual practical cases.

Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study.

Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user's sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.

Estimation of Sleep Stages Analyzing Respiratory and Movement Signals.

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

RR interval analysis for the distinction between stress, physical activity and no activity using a portable ECG.

Methods based exclusively on heart rate hardly allow to differentiate between physical activity, stress, relaxation, and rest, that is why an additional sensor like activity/movement sensor added for detection and classification. The response of the heart to physical activity, stress, relaxation, and no activity can be very similar. In this study, we can observe the influence of induced stress and analyze which metrics could be considered for its detection. The changes in the Root Mean Square of the Successive Differences provide us with information about physiological changes. A set of measurements collecting the RR intervals was taken. The intervals are used as a parameter to distinguish four different stages. Parameters like skin conductivity or skin temperature were not used because the main aim is to maintain a minimum number of sensors and devices and thereby to increase the wearability in the future.

Automatic sleep stages classification using respiratory, heart rate and movement signals.

OBJECTIVE: This paper presents an algorithm for non-invasive sleep stage identification using respiratory, heart rate and movement signals. The algorithm is part of a system suitable for long-term monitoring in a home environment, which should support experts analysing sleep. APPROACH: As there is a strong correlation between bio-vital signals and sleep stages, multinomial logistic regression was chosen for categorical distribution of sleep stages. Several derived parameters of three signals (respiratory, heart rate and movement) are input for the proposed method. Sleep recordings of five subjects were used for the training of a machine learning model and 30 overnight recordings collected from 30 individuals with about 27 000 epochs of 30 s intervals each were evaluated. MAIN RESULTS: The achieved rate of accuracy is 72% for Wake, NREM, REM (with Cohen's kappa value 0.67) and 58% for Wake, Light (N1 and N2), Deep (N3) and REM stages (Cohen's kappa is 0.50). Our approach has confirmed the potential of this method and disclosed several ways for its improvement. SIGNIFICANCE: The results indicate that respiratory, heart rate and movement signals can be used for sleep studies with a reasonable level of accuracy. These inputs can be obtained in a non-invasive way applying it in a home environment. The proposed system introduces a convenient approach for a long-term monitoring system which could support sleep laboratories. The algorithm which was developed allows for an easy adjustment of input parameters that depend on available signals and for this reason could also be used with various hardware systems.

Mild ischemic injury leads to long-term alterations in the kidney: amelioration by spironolactone administration.

Administration of the mineralocorticoid receptor antagonist spironolactone prevents the development of chronic kidney disease (CKD) after a severe ischemic injury. However, whether brief periods of ischemia lead to CKD and whether spironolactone administration after ischemia may be a useful therapeutic strategy to prevent the gradual deterioration of structure and function remains unexplored. Nineteen male Wistar rats were divided into four groups: rats that underwent renal bilateral ischemia for 10, 20, or 45 min were compared with sham operated rats. Additionally, thirteen male Wistar rats that underwent renal bilateral ischemia for 20 min were divided into an untreated ischemic group (I) and two groups receiving spironolactone, 20 mg/kg by gavage, at either 0 (Sp0) or 1.5-h after ischemia (Sp1.5). The rats were followed up and studied after 9 months. Mild (20 min) and severe (45 min) ischemia induced a progressive increase in proteinuria at varying magnitudes, whereas minor ischemia (10 min) did not modify proteinuria. CKD induced by moderate ischemia was characterized by renal hypertrophy and tubulointerstitial fibrosis. These effects were associated with activation of the transforming growth factor ß (TGFß) signaling pathway and up-regulation of endothelin receptor A (ETA) and alpha smooth muscle actin (αSMA). Spironolactone treatment immediately or 1.5-h after the ischemic insult prevented the onset of these disorders. Our results show that moderate ischemic insult leads to long-term structural and molecular changes that may compromise renal function in later stages. Additionally, we demonstrate that spironolactone administration after mild ischemia prevents this detrimental effect.

Intra-renal transfection of heat shock protein 90 alpha or beta (Hsp90α or Hsp90ß) protects against ischemia/reperfusion injury.

BACKGROUND: We previously reported that radicicol (Hsp90 inhibitor) induced a reduction in the renal blood flow and glomerular filtration rate, in part due to a reduction in urinary NO2/NO3 excretion, suggesting that Hsp90 regulates renal vascular tone in physiological conditions. However, there is a lack of information concerning Hsp90α or Hsp90ß role on eNOS activity and their association with acute kidney injury (AKI) characterized by an inadequate NO production. This study evaluated the effects of Hsp90α or Hsp90ß intra-renal transfection under ischemia/reperfusion (IR) injury. METHODS: Uninephrectomized (Nx) rats were intra-renally transfected through injections with Hsp90α or Hsp90ß cloned into pcDNA3.1(+) or empty vector (EV) at 48 h before inducing IR, as indicated in the following groups: (i) Nx+sham, (ii) Nx+IR, (iii) Nx+IR+EV, (iv) Nx+IR+Hsp90α and (v) Nx+IR+Hsp90ß. After 24 h, physiological, histopathological, biochemical and molecular studies were performed. RESULTS: IR-induced renal dysfunction, structural injury, tubular proliferation, the elevation of urinary Hsp72 and the reduction of urinary NO2/NO3 excretion. These alterations were associated with reduced eNOS-Hsp90 coupling and changes in the eNOS phosphorylation state mediated through a reduction in PKCα and increased Rho kinase expression. In contrast, intra-renal transfection of Hsp90α or Hsp90ß prevented IR injury that was associated with the restoration of eNOS-Hsp90 coupling, eNOS activating phosphorylation and PKCα and Rho kinase levels. CONCLUSIONS: Here we showed that eNOS-Hsp90 uncoupling plays a critical role in promoting NO reduction during IR. This effect was effectively reversed through Hsp90α or Hsp90ß intra-renal transfection, suggesting their implication in regulating NO/eNOS pathway and the renal vascular tone.

Signal injection as a fault detection technique.

Double frequency tests are used for evaluating stator windings and analyzing the temperature. Likewise, signal injection on induction machines is used on sensorless motor control fields to find out the rotor position. Motor Current Signature Analysis (MCSA), which focuses on the spectral analysis of stator current, is the most widely used method for identifying faults in induction motors. Motor faults such as broken rotor bars, bearing damage and eccentricity of the rotor axis can be detected. However, the method presents some problems at low speed and low torque, mainly due to the proximity between the frequencies to be detected and the small amplitude of the resulting harmonics. This paper proposes the injection of an additional voltage into the machine being tested at a frequency different from the fundamental one, and then studying the resulting harmonics around the new frequencies appearing due to the composition between injected and main frequencies.

Application of Business Process Management to drive the deployment of a speech recognition system in a healthcare organization.

We present a methodology based on Business Process Management to guide the development of a speech recognition system in a hospital in Spain. The methodology eases the deployment of the system by 1) involving the clinical staff in the process, 2) providing the IT professionals with a description of the process and its requirements, 3) assessing advantages and disadvantages of the speech recognition system, as well as its impact in the organisation, and 4) help reorganising the healthcare process before implementing the new technology in order to identify how it can better contribute to the overall objective of the organisation.

La comunicación en la práctica médica / Communication in medical practice

La comunicación desempeña un rol preponderante dentro de la relación médico-paciente. En el presente trabajo se hace un breve recuento de su significado. Se abordan diferentes aspectos que reflejan la necesidad cada vez más creciente de convertir a los médicos en excelentes comunicadores. Dentro de los diferentes aspectos a tener en cuenta en la práctica médica se hace una profunda reflexión del médico como comunicador por excelencia; se enfatiza en la entrevista y se arriba a conclusiones importantes tales como la necesidad de promover y prevenir salud, así como curar enfermedades apoyados en la función esencial que brinda la comunicación en la labor médica diaria