Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration.

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Is the Median Hourly Ambulatory Heart Rate Range Helpful in Stratifying Mortality Risk among Newly Diagnosed Atrial Fibrillation Patients?

Background: The application of heart rate variability is problematic in patients with atrial fibrillation (AF). This study aims to explore the associations between all-cause mortality and the median hourly ambulatory heart rate range (AHRR˜24hr) compared with other parameters obtained from the Holter monitor in patients with newly diagnosed AF. Material and Methods: A total of 30 parameters obtained from 521 persistent AF patients' Holter monitor were analyzed retrospectively from 1 January 2010 to 31 July 2014. Every patient was followed up to the occurrence of death or the end of 30 June 2017. Results:AHRR˜24hr was the most feasible Holter parameter. Lower AHRR˜24hr was associated with increased risk of all-cause mortality (adjusted hazard ratio [aHR] for every 10-bpm reduction: 2.70, 95% confidence interval [CI]: 1.75-4.17, p < 0.001). The C-statistic of AHRR˜24hr alone was 0.707 (95% CI: 0.658-0.756), and 0.697 (95% CI: 0.650-0.744) for the CHA2DS2-VASc score alone. By combining AHRR˜24hr with the CHA2DS2-VASc score, the C-statistic could improve to 0.764 (95% CI: 0.722-0.806). While using 20 bpm as the cut-off value, the aHR was 3.66 (95% CI: 2.05-6.52) for patients with AHRR˜24hr < 20 bpm in contrast to patients with AHRR˜24hr ≥ 20 bpm. Conclusions:AHRR˜24hr could be helpful for risk stratification for AF in addition to the CHA2DS2-VASc score.

Interpretable morphological features for efficient single-lead automatic ventricular ectopy detection.

OBJECTIVE: We designed an automatic, computationally efficient, and interpretable algorithm for detecting ventricular ectopic beats in long-term, single-lead electrocardiogram recordings. METHODS: We built five simple, interpretable, and computationally efficient features from each cardiac cycle, including a novel morphological feature which described the distance to the median beat in the recording. After an unsupervised subject-specific normalization procedure, we trained an ensemble binary classifier using the AdaBoost algorithm RESULTS: After our classifier was trained on subset DS1 of the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database, our classifier obtained an F1 score of 94.35% on subset DS2 of the same database. The same classifier achieved F1 scores of 92.06% on the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead Arrhythmia database and 91.40% on the MIT-BIH Long-term database. A phenotype-specific analysis of model performance was afforded by the annotations included in the St. Petersburg INCART Arrhythmia database CONCLUSION: The five features this novel algorithm employed allowed our ventricular ectopy detector to obtain high precision on previously unseen subjects and databases SIGNIFICANCE: Our ventricular ectopy detector will be used to study the relationship between premature ventricular contractions and adverse patient outcomes such as congestive heart failure and death.

An adaptive QRS detection algorithm for ultra-long-term ECG recordings.

BACKGROUND: Accurate detection of QRS complexes during mobile, ultra-long-term ECG monitoring is challenged by instances of high heart rate, dramatic and persistent changes in signal amplitude, and intermittent deformations in signal quality that arise due to subject motion, background noise, and misplacement of the ECG electrodes. PURPOSE: We propose a revised QRS detection algorithm which addresses the above-mentioned challenges. METHODS AND RESULTS: Our proposed algorithm is based on a state-of-the-art algorithm after applying two key modifications. The first modification is implementing local estimates for the amplitude of the signal. The second modification is a mechanism by which the algorithm becomes adaptive to changes in heart rate. We validated our proposed algorithm against the state-of-the-art algorithm using short-term ECG recordings from eleven annotated databases available at Physionet, as well as four ultra-long-term (14-day) ECG recordings which were visually annotated at a central ECG core laboratory. On the database of ultra-long-term ECG recordings, our proposed algorithm showed a sensitivity of 99.90% and a positive predictive value of 99.73%. Meanwhile, the state-of-the-art QRS detection algorithm achieved a sensitivity of 99.30% and a positive predictive value of 99.68% on the same database. The numerical efficiency of our new algorithm was evident, as a 14-day recording sampled at 200 Hz was analyzed in approximately 157 s. CONCLUSIONS: We developed a new QRS detection algorithm. The efficiency and accuracy of our algorithm makes it a good fit for mobile health applications, ultra-long-term and pathological ECG recordings, and the batch processing of large ECG databases.

Sleep-wake classification via quantifying heart rate variability by convolutional neural network.

OBJECTIVE: Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series. APPROACH: We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities. MAIN RESULTS: On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics. SIGNIFICANCE: This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.

Single-lead f-wave extraction using diffusion geometry.

OBJECTIVE: A novel single-lead f-wave extraction algorithm based on the modern diffusion geometry data analysis framework is proposed. APPROACH: The algorithm is essentially an averaged beat subtraction algorithm, where the ventricular activity template is estimated by combining a newly designed metric, the 'diffusion distance', and the non-local Euclidean median based on the non-linear manifold setup. We coined the algorithm [Formula: see text]. MAIN RESULTS: Two simulation schemes are considered, and the new algorithm [Formula: see text] outperforms traditional algorithms, including the average beat subtraction, principal component analysis, and adaptive singular value cancellation, in different evaluation metrics with statistical significance. SIGNIFICANCE: The clinical potential is shown in the real Holter signal, and we introduce a new score to evaluate the performance of the algorithm.

Clinical usage and influence of radiographs in the assessment of hallux valgus.

It has been assumed that radiographs are consistently used in the preoperative evaluation of hallux valgus; however, little information is available to support this assumption. To investigate the frequency of use and clinical utility of radiographs in the assessment of hallux valgus, an online survey was completed by 28 podiatrists in UK departments of podiatric surgery. Radiographs were used in the assessment of hallux valgus in all departments. Of the 28 podiatrists, 61% viewed digital radiographs only, 7% film only, and 32%, a combination of both. Also, 71% routinely took measurements from the radiographs to inform procedure selection. Of those taking measurements, the first intermetatarsal angle was the only measurement used by all participants, followed by the sesamoid position, used by 90%. For those taking measurements, the measurements influenced procedure selection for most cases before surgery, with the procedure site within the first metatarsal moving more proximally as the first metatarsal angle increased.