Parsimonious Waveform-derived Features consisting of Pulse Arrival Time and Heart Rate Variability Predicts the Onset of Septic Shock

Sepsis is a major public health emergency and one of the leading causes of morbidity and mortality in critically ill patients. For each hour treatment is delayed, shock-related mortality increases, so early diagnosis and intervention is of utmost importance. However, earlier recognition of shock requires active monitoring, which may be delayed due to subclinical manifestations of the disease at the early phase of onset. Machine learning systems can increase timely detection of shock onset by exploiting complex interactions among continuous physiological waveforms. We use a dataset consisting of high-resolution physiological waveforms from intensive care unit (ICU) of a tertiary hospital system. We investigate the use of mean arterial blood pressure (MAP), pulse arrival time (PAT), heart rate variability (HRV), and heart rate (HR) for the early prediction of shock onset. Using only five minutes of the aforementioned vital signals from 239 ICU patients, our developed models can accurately predict septic shock onset 6 to 36 hours prior to clinical recognition with area under the receiver operating characteristic (AUROC) of 0.84 and 0.8 respectively. This work lays foundations for a robust, efficient, accurate and early prediction of septic shock onset which may help clinicians in their decision-making processes. This study introduces machine learning models that provide fast and accurate predictions of septic shock onset times up to 36 hours in advance. BP, PAT and HR dynamics can independently predict septic shock onset with a look-back period of only 5 mins.

Effect of transcutaneous cervical vagus nerve stimulation on declarative and working memory in patients with Posttraumatic Stress Disorder (PTSD): A pilot study

BACKGROUND

Posttraumatic stress disorder (PTSD) is associated with changes in multiple neurophysiological systems, including verbal declarative memory deficits. Vagus Nerve Stimulation (VNS) has been shown in preliminary studies to enhance function when paired with cognitive and motor tasks. The purpose of this study was to analyze the effect of transcutaneous cervical VNS (tcVNS) on attention, declarative and working memory in PTSD patients.

METHODS

Fifteen PTSD patients were randomly assigned to active tcVNS (N = 8) or sham (N = 7) stimulation in a double-blinded fashion. Memory assessment tests including paragraph recall and N-back tests were performed to assess declarative and working memory function when paired with active/sham tcVNS once per month in a longitudinal study during which patients self-administered tcVNS/sham twice daily.

RESULTS

Active tcVNS stimulation resulted in a significant improvement in paragraph recall performance following pairing with paragraph encoding for PTSD patients at two months (p < 0.05). It resulted in a 91 % increase in paragraph recall performance within group (p = 0.03), while sham tcVNS exhibited no such trend in performance improvement. In the N-back study, positive deviations in accuracy, precision and recall measures on different day visits (7,34,64,94) of patients with respect to day 1 revealed a pattern of better performance of the active tcVNS population compared to sham VNS which did not reach statistical significance.

LIMITATIONS

Our sample size was small.

CONCLUSIONS

These preliminary results suggest that tcVNS improves attention, declarative and working memory, which may improve quality of life and productivity for patients with PTSD. Future studies are required to confirm these results.

Automatic Detection of Aortic Valve Opening Using Seismocardiography in Healthy Individuals

Accurate detection of fiducial points in a seismocardiogram (SCG) is a challenging research problem for its clinical application. In this paper, an automated method for detecting aortic valve opening (AO) instants using the dorso-ventral component of the SCG signal is proposed. This method does not require electrocardiogram (ECG) as a reference signal. After preprocessing the SCG, multiscale wavelet decomposition is carried out to get signal components in different wavelet subbands. The subbands having possible AO peaks are selected by a newly proposed dominant-multiscale-kurtosis- and dominant-multiscale-central-frequency-based criterion. The signal is reconstructed using selected subbands, and it is emphasized using the weights derived from the proposed relative squared dominant multiscale kurtosis. The Shannon energy followed by autocorrelation coefficients is computed for systole envelope construction. Finally, AO peaks are detected by a Gaussian-derivative-filtering-based scheme. The robustness of the proposed method is tested using clean and noisy SCG signals from the combined measurement of ECG, breathing, and SCG database. Evaluation results show that the method can achieve an average sensitivity of 94%, a prediction rate of 90%, and a detection accuracy of 86% approximately over 4585 analyzed beats.

Robust detection of premature ventricular contractions using sparse signal decomposition and temporal features

An automated noise-robust premature ventricular contraction (PVC) detection method is proposed based on the sparse signal decomposition, temporal features, and decision rules. In this Letter, the authors exploit sparse expansion of electrocardiogram (ECG) signals on mixed dictionaries for simultaneously enhancing the QRS complex and reducing the influence of tall P and T waves, baseline wanders, and muscle artefacts. They further investigate a set of ten generalised temporal features combined with decision-rule-based detection algorithm for discriminating PVC beats from non-PVC beats. The accuracy and robustness of the proposed method is evaluated using 47 ECG recordings from the MIT/BIH arrhythmia database. Evaluation results show that the proposed method achieves an average sensitivity of 89.69%, and specificity 99.63%. Results further show that the proposed decision-rule-based algorithm with ten generalised features can accurately detect different patterns of PVC beats (uniform and multiform, couplets, triplets, and ventricular tachycardia) in presence of other normal and abnormal heartbeats.