Abstract The objective is to develop a non-invasive automatic method for detection of epileptic seizures with motor manifestations. Ten healthy subjects who simulated seizures and one patient ...participated in the study. Surface electromyography (sEMG) and motion sensor features were extracted as energy measures of reconstructed sub-bands from the discrete wavelet transformation (DWT) and the wavelet packet transformation (WPT). Based on the extracted features all data segments were classified using a support vector machine (SVM) algorithm as simulated seizure or normal activity. A case study of the seizure from the patient showed that the simulated seizures were visually similar to the epileptic one. The multi-modal intelligent seizure acquisition (MISA) system showed high sensitivity, short detection latency and low false detection rate. The results showed superiority of the multi-modal detection system compared to the uni-modal one. The presented system has a promising potential for seizure detection based on multi-modal data.
Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and ...implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients’ circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6, ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772–0.993), but lower for the SAEs (AUROC: 0.594–0.611). The time of early warning for the EWS events were 2.8–5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.
Quantitative electroencephalography from freely moving rats is commonly used as a translational tool for predicting drug‐effects in humans. We hypothesized that drug‐effects may be expressed ...differently depending on whether the rat is in active locomotion or sitting still during recording sessions, and proposed automatic state‐detection as a viable tool for estimating drug‐effects free of hypo‐/hyperlocomotion‐induced effects. We aimed at developing a fully automatic and validated method for detecting two behavioural states: active and inactive, in one‐second intervals and to use the method for evaluating ketamine, DOI, d‐cycloserine, d‐amphetamine, and diazepam effects specifically within each state. The developed state‐detector attained high precision with more than 90% of the detected time correctly classified, and multiple differences between the two detected states were discovered. Ketamine‐induced delta activity was found specifically related to locomotion. Ketamine and DOI suppressed theta and beta oscillations exclusively during inactivity. Characteristic gamma and high‐frequency oscillations (HFO) enhancements of the NMDAR and 5HT2A modulators, speculated associated with locomotion, were profound and often largest during the inactive state. State‐specific analyses, theoretically eliminating biases from altered occurrence of locomotion, revealed only few effects of d‐amphetamine and diazepam. Overall, drug‐effects were most abundant in the inactive state. In conclusion, this new validated and automatic locomotion state‐detection method enables fast and reliable state‐specific analysis facilitating discovery of state‐dependent drug‐effects and control for altered occurrence of locomotion. This may ultimately lead to better cross‐species translation of electrophysiological effects of pharmacological modulations.
An automatic method for estimating drug effects on neuronal oscillatory activity in two behavioural states: active and inactive, was developed. Testing the method on ketamine, d‐cycloserine, DOI, amphetamine, and diazepam, we found that drug‐effects depended on whether the rat was in active locomotion or sitting still during recording sessions. Fast and reliable state‐specific analyses may greatly improve the translational value of rodent pharmaco‐encephalography towards human EEG studies.
Monitoring of high-risk patients in hospital wards is crucial in identifying and preventing clinical deterioration. Sympathetic nervous system activity measured continuously and non-invasively by ...Electrodermal activity (EDA) may relate to complications, but the clinical use remains untested. The aim of this study was to explore associations between deviations of EDA and subsequent serious adverse events (SAE). Patients admitted to general wards after major abdominal cancer surgery or with acute exacerbation of chronic obstructive pulmonary disease were continuously EDA-monitored for up to 5 days. We used time-perspectives consisting of 1, 3, 6, and 12 h of data prior to first SAE or from start of monitoring. We constructed 648 different EDA-derived features to assess EDA. The primary outcome was any SAE and secondary outcomes were respiratory, infectious, and cardiovascular SAEs. Associations were evaluated using logistic regressions with adjustment for relevant confounders. We included 714 patients and found a total of 192 statistically significant associations between EDA-derived features and clinical outcomes. 79% of these associations were EDA-derived features of absolute and relative increases in EDA and 14% were EDA-derived features with normalized EDA above a threshold. The highest F1-scores for primary outcome with the four time-perspectives were 20.7–32.8%, with precision ranging 34.9–38.6%, recall 14.7–29.4%, and specificity 83.1–91.4%. We identified statistically significant associations between specific deviations of EDA and subsequent SAE, and patterns of EDA may be developed to be considered indicators of upcoming clinical deterioration in high-risk patients.
Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using ...2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.
Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) may rapidly require intensive care treatment. Evaluation of vital signs is necessary to detect physiological abnormalities (micro ...events), but patients may deteriorate between measurements. We aimed to assess if continuous monitoring of vital signs in patients admitted with AECOPD detects micro events more often than routine ward rounds. In this observational pilot study (NCT03467815), 30 adult patients admitted with AECOPD were included. Patients were continuously monitored with peripheral oxygen saturation (SpO
2
), heart rate, and respiratory rate during the first 4 days after admission. Hypoxaemic events were defined as decreased SpO
2
for at least 60 s. Non-invasive blood pressure was also measured every 15–60 min. Clinical ward staff measured vital signs as part of Early Warning Score (EWS). Data were analysed using Fisher’s exact test or Wilcoxon rank sum test. Continuous monitoring detected episodes of SpO
2
< 92% in 97% versus 43% detected by conventional EWS (p < 0.0001). Events of SpO
2
< 88% was detected in 90% with continuous monitoring compared with 13% with EWS (p < 0.0001). Sixty-three percent of patients had episodes of SpO
2
< 80% recorded by continuous monitoring and 17% had events lasting longer than 10 min. No events of SpO
2
< 80% was detected by EWS. Micro events of tachycardia, tachypnoea, and bradypnoea were also more frequently detected by continuous monitoring (p < 0.02 for all). Moderate and severe episodes of desaturation and other cardiopulmonary micro events during hospitalization for AECOPD are common and most often not detected by EWS.
Rapid eye movement (REM) sleep without atonia detection is a prerequisite for diagnosis of REM sleep behavior disorder (RBD). As the visual gold standard method is time-consuming and subjective, ...several automated methods have been proposed. This study aims to compare their performances: The REM atonia index (RAI), the supra-threshold-REM-activity metric, the Frandsen index, the short/long muscle activity indices, and the Kempfner index algorithms were applied to 27 healthy control participants (C), 25 patients with Parkinson's disease (PD) without RBD (PD-RBD), 29 patients with PD and RBD (PD + RBD), 29 idiopathic patients with RBD, and 36 patients with periodic limb movement disorder (PLMD). The indices were calculated in various configurations: (1) considering all muscle activities; (2) excluding the ones related to arousals; (3) excluding the ones during apnea events; (4) excluding the ones before and after apnea events; (5) combining configurations 2 and 3; and (6) combining configurations 2 and 4. For each of these configurations, the discrimination capability of the indices was tested for the following comparisons: (1) (C, PD-RBD, PLMD) vs (PD + RBD, RBD); (2) C vs RBD; (3) PLMD vs RBD; (4) C vs PD-RBD; (5) C vs PLMD; (6) PD-RBD vs PD + RBD; and (7) C vs PLMD vs RBD. Results showed varying methods' performances across the different configurations and comparisons, making it impossible to identify the optimal method and suggesting the need of further improvements. Nevertheless, RAI seems the most sensible one for RBD detection. Moreover, apnea and arousal-related movements seem not to influence the algorithms' performances in patients' classification.
Continuous monitoring of high-risk patients and early prediction of severe outcomes is crucial to prevent avoidable deaths. Current clinical monitoring is primarily based on intermittent observation ...of vital signs and the early warning scores (EWS). The drawback is lack of time series dynamics and correlations among vital signs. This study presents an approach to real-time outcome prediction based on machine learning from continuous recording of vital signs. Systolic blood pressure, diastolic blood pressure, heart rate, pulse rate, respiration rate and peripheral blood oxygen saturation were continuously acquired by wearable devices from 292 post-operative high-risk patients. The outcomes from serious complications were evaluated based on review of patients’ medical record. The descriptive statistics of vital signs and patient demographic information were used as features. Four machine learning models K-Nearest-Neighbors (KNN), Decision Trees (DT), Random Forest (RF), and Boosted Ensemble (BE) were trained and tested. In static evaluation, all four models had comparable prediction performance to that of the state of the art. In dynamic evaluation, the models trained from the static evaluation were tested with continuous data. RF and BE obtained the lower false positive rate (FPR) of 0.073 and 0.055 on no-outcome patients respectively. The four models KNN, DT, RF and BE had area under receiver operating characteristic curve (AUROC) of 0.62, 0.64, 0.65 and 0.64 respectively on outcome patients. RF was found to be optimal model with lower FPR on no-outcome patients and a higher AUROC on outcome patients. These findings are encouraging and indicate that additional investigations must focus on validating performance in a clinical setting before deployment of the real-time outcome prediction.
•Real-time prediction of severe outcomes was carried out on post-operative patients.•The severe outcomes are from neurological, respiratory, circulatory, infectious, and other complications.•Machine learning based methods have been implemented and compared.•In static evaluation, the performance of prediction of severe outcomes is comparable to that of the state of the art.•In dynamic evaluation, real-time performance achieves an AUROC of 0.65.
Background
Patients undergoing major surgery are at risk of complications, so‐called serious adverse events (SAE). Continuous monitoring may detect deteriorating patients by recording abnormal vital ...signs. We aimed to assess the association between abnormal vital signs inspired by Early Warning Score thresholds and subsequent SAEs in patients undergoing major abdominal surgery.
Methods
Prospective observational cohort study continuously monitoring heart rate, respiratory rate, peripheral oxygen saturation, and blood pressure for up to 96 h in 500 postoperative patients admitted to the general ward. Exposure variables were vital sign abnormalities, primary outcome was any serious adverse event occurring within 30 postoperative days. The primary analysis investigated the association between exposure variables per 24 h and subsequent serious adverse events.
Results
Serious adverse events occurred in 37% of patients, with 38% occurring during monitoring. Among patients with SAE during monitoring, the median duration of vital sign abnormalities was 272 min (IQR 110–447), compared to 259 min (IQR 153–394) in patients with SAE after monitoring and 261 min (IQR 132–468) in the patients without any SAE (p = .62 for all three group comparisons). Episodes of heart rate ≥110 bpm occurred in 16%, 7.1%, and 3.9% of patients in the time before SAE during monitoring, after monitoring, and without SAE, respectively (p < .002). Patients with SAE after monitoring experienced more episodes of hypotension ≤90 mm Hg/24 h (p = .001).
Conclusion
Overall duration of vital sign abnormalities at current thresholds were not significantly associated with subsequent serious adverse events, but more patients with tachycardia and hypotension had subsequent serious adverse events.
Trial registration
Clinicaltrials.gov, identifier NCT03491137.
Background
Patients admitted to the emergency care setting with COVID‐19‐infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still ...unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to ICU or having fatal outcome vs. those that were not.
Methods
Two wireless sensors continuously monitored (CM) respiratory rate (RR), heart rate (HR), and peripheral arterial oxygen saturation (SpO2). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score (NEWS).
Results
SpO2 < 92% for more than 60 min was detected in 92% of the patients with CM vs. 40% with NEWS (p < .00001). RR > 24 breaths per minute for more than 5 min were detected in 70% with CM vs. 33% using NEWS (p = .0001). HR ≥ 111 for more than 60 min was seen in 51% with CM and 22% with NEWS (p = .0002). Patients admitted to ICU or having fatal outcome had longer durations of RR > 24 brpm (p = .01), RR > 21 brpm (p = .01), SpO2 < 80% (p = .01), and SpO2 < 85% (p = .02) compared to patients that were not.
Conclusion
Episodes of desaturation and tachypnea in hospitalized patients with COVID‐19 infection are common and often not detected by routine measurements.
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