Long-term Ambulatory Monitoring Research Articles

SPHYNCS: Feasibility of long-term monitoring with Fitbit smartwatches in central disorders of hypersomnolence and extraction of digital biomarkers in narcolepsy.

The Swiss Primary Hypersomnolence and Narcolepsy Cohort Study (SPHYNCS) is a multicenter research initiative to identify new biomarkers in central disorders of hypersomnolence (CDH). Whereas narcolepsy type 1 (NT1) is well characterized, other CDH disorders lack precise biomarkers. In SPHYNCS, we utilized Fitbit smartwatches to monitor physical activity, heart rate, and sleep parameters over one year. We examined the feasibility of long-term ambulatory monitoring using the wearable device. We then explored digital biomarkers differentiating patients with NT1 from healthy controls (HC). A total of 115 participants received a Fitbit smartwatch. Using a compliance metric to evaluate the usability of the wearable device, we found an overall compliance rate of 80% over one year. We calculated daily physical activity, heart rate, and sleep parameters from two weeks of greatest compliance to compare NT1 (n=20) and HC (n=9) subjects. Compared to controls, NT1 patients demonstrated findings consistent with increased sleep fragmentation, including significantly greater wake-after-sleep onset (p=0.007) and awakening index (p=0.025), as well as standard deviation of time in bed (p=0.044). Moreover, NT1 patients exhibited a significantly shorter REM latency (p=0.019), and sleep latency (p=0.001), as well as a lower peak heart rate (p=0.008), heart rate standard deviation (p=0.039) and high-intensity activity (p=0.009) compared to HC. This ongoing study demonstrates the feasibility of long-term monitoring with wearable technology in patients with CDH and potentially identifies a digital biomarker profile for NT1. While further validation is needed in larger datasets, these data suggest that long-term wearable technology may play a future role in diagnosing and managing narcolepsy.

Long-term ambulatory monitoring and identification of digital biomarkers in narcolepsy

Long-term ambulatory monitoring and identification of digital biomarkers in narcolepsy

A Review on Direct Digital Conversion Techniques for Biomedical Signal Acquisition

Biomedical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG) and photoplethysmography (PPG) are recorded routinely to provide helpful information for early diagnosis of disease. Low power consumption is very important to allow long-term ambulatory monitoring with battery-powered systems. A direct digital conversion (DDC) technique has been proposed in recent years, which employs preamplifier and data converters, reducing the complexity of the readout chain and thus its power consumption. This paper provides a review on DDC for biopotential signals and bio-optical signal acquisition. The state-of-the-art DDC-based readout architectures together with circuit implementations are provided.

PO-04-188 PREMATURE VENTRICULAR COMPLEXES: ASSESSING BURDEN DENSITY IN A LARGE NATIONAL COHORT POPULATION TO BETTER DEFINE OPTIMAL ECG MONITORING DURATION

PVC burden frequency has been shown to correlate with the incidence of PVC-mediated cardiomyopathy. A single center study of 107 patients wearing Zio monitors indicated that measurement of PVC burden for 24-48 hours may not reflect the total burden. This study aims to assess PVC burden density, and the significance of long-term monitoring in a large national cohort. The objective of this study is to establish the optimal duration of ECG monitoring required to accurately characterize PVC burden severity and other significant factors. Patients with a PVC burden ≥5%, age ≥18 and a ≥13-day wear period were analyzed between June 2011 to April 2022. Patient demographics were reported, and the mean absolute error in PVC burden was calculated at baseline (<10 sec), 24 hours, 48 hours, and daily to14 days. Burden at the end of the wear period was considered the true value for PVC burden. The measured error was described for each subinterval (mean, SD, median, IQR). The accuracy in PVC burden was evaluated across monitoring durations at 10% (low),15% (moderate) and 20% (high) thresholds. There were 106,705 patients analyzed, 33.6% of patients were females with an average age of 67.3±15.4 years old. PVC density was more frequent in males than females, 66:34 respectively and in those older than 65 years of age. The PVC burden mean absolute error was calculated at baseline (<10sec), 24 hours, 48 hours, 7 days, and 13 days (Figure1). When comparing PVC burden densities with lower and higher than 20% at 24 hours and 7 days, data showed a decreased in mean absolute error from 2.78% and 3.97% at 24 hours to 1.27% and 1.75% at 7 days. Furthermore, the accuracy in categorization of PVC burden at 15% threshold increased from 89.2% at 24 hours to 95.1% at 7 days. Most common concerning arrhythmias detected in patients with PVC burden do show important delineating trend to consider (Table1). In this large national cohort, the reduction in PVC burden mean absolute error from 24 hours through 14 days underscores the importance of long-term ambulatory monitoring and extends prior study results. Increased monitoring duration through at least 7-10 days is associated with improved accuracy in categorization of patients by PVC burden level. Patients with PVC burden <20% may benefit from longer monitoring compared to >20% burden population. Concomitant arrythmias including VT and Afib in patient with PVC burden further emphasized the significance of closer monitoring and early detection.Tabled 1Table1Total Device Count (%)Low (5%-10%)* n=59,702Moderate(10%-20%)* n=34,605High (>20%)* n=12,398Overall* n=106,705VT37,858 (63.4)23,799 (68.8)9,251 (74.6)70,908 (66.5)AFIB9,021 (15.1)4,123 (11.9)1,119 (9.0)14,263 (13.4)SVT46,068 (77.2)26,712 (77.2)9,495 (76.6)82,275 (77.1)AVB1,467 (2.5)671 (1.9)181 (1.5)2,319 (2.2)Pause (≥3 sec)3,479 (5.8)1,682 (4.9)466 (3.8)5,627 (5.3) Open table in a new tab

A service evaluation of patch technology compared with traditional holter monitoring in a stroke pathway

Abstract Background Atrial fibrillation (AF) is a known risk factor for embolic stroke. Establishing a diagnosis of AF will guide clinical decision making and help with downstream secondary stroke prevention. Patch technology is increasingly used for long-term cardiac monitoring for arrhythmia detection, as healthcare organisations look for new innovative ways to streamline service delivery. We were selected as a host site to evaluate the service and patient benefits of Zio XT in AF detection for cryptogenic stroke, as part a Artificial Intelligence Award. Purpose To evaluate the service and patient benefits of patch technology versus traditional Holter in a Stroke / TIA pathway. Methods The Zio XT data were collected between March and December 2021. A total of 778 patients were included. We only selected patients who were referred for a long-term ambulatory monitoring following a recent diagnosis of stroke or transient ischaemic attack (TIA) of an unknown cause (cryptogenic). The stroke pathway operates across three sites and all were included. We undertook a retrospective analysis of our previous pathway using Holter monitoring and sampled 100 stroke / TIA patients who underwent 24–72 hours of monitoring between January to June 2019. In order to obtain the requisite data a variety of sources were scrutinised: referral letters, test reports, patient medical records, internal clinical systems and third-provider platforms. Results The comparison of the ECG technologies showed the Zio XT used less staff time. We found a 50% reduction in-clinic monitor fitting when compared with Holter. Zio XT were all analysed by the provider staff resulting in significant savings in service resource time for analysis. The Zio XT had a faster test turnaround time (referral to report available), 19 days (inclusive of 14 days wear time) for Zio compared with 60 days for Holters. The rapid Zio turnaround led to a 27.5% reduction in repeat clinic appointments, due to results being unavailable for the consultation (table 1). Zio XT also had fewer repeat testing due to inconclusive results, 1% for Zio versus 16% for Holter. AF yield did not vary significantly between technologies, 3% yield for both Holter and Zio, which could be due to the young mean age of the stroke/ TIA the patient cohort: 63.8 years for Holter versus 62.9 years for Zio XT. Conclusion Introducing new ECG technologies such as Zio XT could increase outpatient and diagnostic capacity due to patches being more readily available than Holters, reduced reliance on clinical staff for fitting and analysis as well as fewer repeat clinic appointments. Overall, access to rapid long term cardiac monitoring has benefits over traditional Holter monitoring for TIA/stroke patients and service delivery. Funding Acknowledgement Type of funding sources: Public Institution(s). Main funding source(s): AI award from the Accelerated Access Collaborative (ACC) in partnership with NHSX and National Institute for Health Research

Deepaware: A hybrid deep learning and context-aware heuristics-based model for atrial fibrillation detection

BackgroundState-of-the-art automatic atrial fibrillation (AF) detection models trained on RR-interval (RRI) features generally produce high performance on standard benchmark electrocardiogram (ECG) AF datasets. These models, however, result in a significantly high false positive rates (FPRs) when applied on ECG data collected under free-living ambulatory conditions and in the presence of non-AF arrhythmias. MethodThis paper proposes DeepAware, a novel hybrid model combining deep learning (DL) and context-aware heuristics (CAH), which reduces the FPR effectively and improves the AF detection performance on participant-operated ambulatory ECG from free-living conditions. It exploits the RRI and P-wave features, as well as the contextual features from the ambulatory ECG. ResultsDeepAware is shown to be very generalizable and superior to the state-of-the-art models when applied on unseen benchmark ECG AF datasets. Most importantly, the model is able to detect AF efficiently when applied on participant-operated ambulatory ECG recordings from free-living conditions and has achieved a sensitivity (Se), specificity (Sp), and accuracy (Acc) of 97.94%, 98.39%, 98.06%, respectively. Results also demonstrate the effect of atrial activity analysis (via P-waves detection) and CAH in reducing the FPR over the RRI features-based AF detection model. ConclusionsThe proposed DeepAware model can substantially reduce the physician’s workload of manually reviewing the false positives (FPs) and facilitate long-term ambulatory monitoring for early detection of AF.

A comparative study of AI systems for epileptic seizure recognition based on EEG or ECG.

The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) presents a simpler and more wearable alternative for long-term ambulatory monitoring. To assess the performance of EEG and ECG signals, AI systems offer a promising way forward for developing high performing models in securing both a reasonable sensitivity and specificity. There are crucial needs for these AI systems to be developed with more clinical relevance and inference generalization. In this work, we implement an ECG-specific convolutional neural network (CNN) model with residual layers and an EEG-specific convolutional long short-term memory (ConvLSTM) model. We trained, validated, and tested these models on a publicly accessible Temple University Hospital (TUH) dataset for reproducibility and performed a non-patient-specific inference-only test on patient EEG and ECG data of The Royal Prince Alfred Hospital (RPAH) in Sydney, Australia. We selected 31 adult patients to balance groups with the following seizure types: generalized, frontal, frontotemporal, temporal, parietal, and unspecific focal epilepsy. Our tests on both EEG and ECG of these patients achieve an AUC score of 0.75. Our results show ECG outperforms EEG with an average improvement of 0.21 and 0.11 AUC score in patients with frontal and parietal focal seizures, respectively.Clinical relevance-Prior research has demonstrated the value of using ECG for seizure documentation. It is believed that specific epileptic foci (seizure origin) may involve network inputs to the autonomic nervous system. Our result indicates that ECG could outperform EEG for individuals with specific seizure origin, particularly in the frontal and parietal lobes.

Refractory Angina Confounded by Preexcitation Syndrome.

Scenario: This 12-lead electrocardiogram (ECG) was obtained on a 48-year-old man who came to the emergency department with progressively worsening left lateral crushing chest pain and dyspnea of 3 hours’ duration. The patient drinks 9 beers daily, is an active smoker, and is treated for hypertension and hypercholesterolemia. His initial vital signs during prehospital transport showed a heart rate of ~110/minute and blood pressure of 151/100 mm Hg. Paramedics administered aspirin and sublingual nitroglycerin during ambulance transport, and this ECG was obtained upon arrival in the emergency department.Sinus rhythm with Wolff-Parkinson-White (WPW) syndrome type A.In the normal electrical conduction system, the atrioventricular (AV) node serves as a gatekeeper to regulate the propagation of signals from the atria into the ventricles. When an accessory pathway exists, ventricular activation occurs through both the AV node and the extra anatomical pathway, yielding a shorter PR interval and a slurred upstroke of the QRS complex (ie, delta wave). When the accessory pathway connects to the left ventricle, the preexcitation starts in the left ventricle first before it proceeds normally through the bundle of His and bundle branches (called WPW type A). This syndrome manifests as a wide QRS complex with pseudo right bundle branch block pattern (ie, tall R wave in right precordial leads). Because of the temporal heterogeneity in ventricular activation, repolarization abnormalities (or non-specific ST-segment and T-wave changes) are common, including early repolarization pattern (eg, J-point elevation best seen in lead V3 in this example). Such dispersion in ventricular depolarization and repolarization might lead to back propagation of the signal to the atria either through the bundle of His or through the accessory pathway. This back propagation may create an atrioventricular reentry circuit and sustained tachycardia. Although WPW syndrome is usually benign, curative catheter ablation is considered in symptomatic or hemodynamically unstable patients.Although secondary repolarization changes are common in WPW syndrome, the ST-segment elevation in the anterior wall that is accompanied with reciprocal changes in the inferolateral walls, coupled with the highly suspicious presenting symptoms and history, are worrisome for acute myocardial infarction. In this specific patient, the initial cardiac workup was negative, and the patient was admitted to the telemetry unit for serial ECGs and troponin assays. Owing to persistent refractory angina, the patient was referred for cardiac angiography the following morning, which revealed normal left ventricular function and mild atherosclerotic disease in the right coronary artery at 20% to 30% occlusion. The patient was discharged home on oral anti-ischemic therapy (eg, aspirin, nitroglycerin) with a referral to cardiac electrophysiology for further management of the underlying WPW syndrome. Long-term ambulatory monitoring (eg, Holter ECG) is warranted to rule out reentry tachyarrhythmias.

Utilization of Long-term Ambulatory Reflux Monitoring as a Guide for Future Treatment in Gastroesophageal Reflux Disease with Inappropriate Response to Proton Pump Inhibitors

Utilization of Long-term Ambulatory Reflux Monitoring as a Guide for Future Treatment in Gastroesophageal Reflux Disease with Inappropriate Response to Proton Pump Inhibitors

Intracranial EEG Validation of Single-Channel Subgaleal EEG for Seizure Identification.

A device that provides continuous, long-term, accurate seizure detection information to providers and patients could fundamentally alter epilepsy care. Subgaleal (SG) EEG is a promising modality that offers a minimally invasive, safe, and accurate means of long-term seizure monitoring. Subgaleal EEG electrodes were placed, at or near the cranial vertex, simultaneously with intracranial EEG electrodes in 21 epilepsy patients undergoing intracranial EEG studies for up to 13 days. A total of 219, 10-minute single-channel SGEEG samples, including 138 interictal awake or sleep segments and 81 seizures (36 temporal lobe, 32 extra-temporal, and 13 simultaneous temporal/extra-emporal onsets) were reviewed by 3 expert readers blinded to the intracranial EEG results, then analyzed for accuracy and interrater reliability. Using a single-channel of SGEEG, reviewers accurately identified 98% of temporal and extratemporal onset, intracranial, EEG-verified seizures with a sensitivity of 98% and specificity of 99%. All focal to bilateral tonic--clonic seizures were correctly identified. Single-channel SGEEG, placed at or near the vertex, reliably identifies focal and secondarily generalized seizures. These findings demonstrate that the SG space at the cranial vertex may be an appropriate site for long-term ambulatory seizure monitoring.

A Simple Low-Cost Wearable Sensor for Long-Term Ambulatory Monitoring of Knee Joint Kinematics.

Accurate monitoring of joint kinematics in individuals with neuromuscular and musculoskeletal disorders within ambulatory settings could provide important information about changes in disease status and the effectiveness of rehabilitation programs and/or pharmacological treatments. This paper introduces a reliable, power efficient, and low-cost wearable system designed for the long-term monitoring of joint kinematics in ambulatory settings. Seventeen healthy subjects wore a retractable string sensor, fixed to two anchor points on the opposing segments of the knee joint, while walking at three different self-selected speeds. Joint angles were estimated from calibrated sensor values and their derivatives in a leave-one-subject-out cross validation manner using a random forest algorithm. The proposed system estimated knee flexion/extension angles with a root mean square error (RMSE) of 5.0°±1.0° across the study subjects upon removal of a single outlier subject. The outlier was likely a result of sensor miscalibration. The proposed wearable device can accurately estimate knee flexion/extension angles during locomotion at various walking speeds. We believe that our novel wearable technology has great potential to enable joint kinematic monitoring in ambulatory settings and thus provide clinicians with an opportunity to closely monitor joint recovery, develop optimal, personalized rehabilitation programs, and ultimately maximize therapeutic outcomes.

Evaluation and Management of Premature Ventricular Complexes.

Premature ventricular complexes (PVCs) are extremely common, found in the majority of individuals undergoing long-term ambulatory monitoring. Increasing age, a taller height, a higher blood pressure, a history of heart disease, performance of less physical activity, and smoking each predict a greater PVC frequency. Although the fundamental causes of PVCs remain largely unknown, potential mechanisms for any given PVC include triggered activity, automaticity, and reentry. PVCs are commonly asymptomatic but can also result in palpitations, dyspnea, presyncope, and fatigue. The history, physical examination, and 12-lead ECG are each critical to the diagnosis and evaluation of a PVC. An echocardiogram is indicated in the presence of symptoms or particularly frequent PVCs, and cardiac magnetic resonance imaging is helpful when the evaluation suggests the presence of associated structural heart disease. Ambulatory monitoring is required to assess PVC frequency. The prognosis of those with PVCs is variable, with ongoing uncertainty regarding the most informative predictors of adverse outcomes. An increased PVC frequency may be a risk factor for heart failure and death, and the resolution of systolic dysfunction after successful catheter ablation of PVCs demonstrates that a causal relationship can be present. Patients with no or mild symptoms, a low PVC burden, and normal ventricular function may be best served with simple reassurance. Either medical treatment or catheter ablation are considered first-line therapies in most patients with PVCs associated with symptoms or a reduced left ventricular ejection fraction, and patient preference plays a role in determining which to try first. If medical treatment is selected, either β-blockers or nondihydropyridine calcium channel blockers are reasonable drugs in patients with normal ventricular systolic function. Other antiarrhythmic drugs should be considered if those initial drugs fail and ablation has been declined, has been unsuccessful, or has been deemed inappropriate. Catheter ablation is the most efficacious approach to eradicate PVCs but may confer increased upfront risks. Original research remains necessary to identify individuals at risk for PVC-induced cardiomyopathy and to identify preventative and therapeutic approaches targeting the root causes of PVCs to maximize effectiveness while minimizing risk.

Mesial temporal resection following long-term ambulatory intracranial EEG monitoring with a direct brain-responsive neurostimulation system.

ObjectiveTo describe seizure outcomes in patients with medically refractory epilepsy who had evidence of bilateral mesial temporal lobe (MTL) seizure onsets and underwent MTL resection based on chronic ambulatory intracranial EEG (ICEEG) data from a direct brain‐responsive neurostimulator (RNS) system.MethodsWe retrospectively identified all patients at 17 epilepsy centers with MTL epilepsy who were treated with the RNS System using bilateral MTL leads, and in whom an MTL resection was subsequently performed. Presumed lateralization based on routine presurgical approaches was compared to lateralization determined by RNS System chronic ambulatory ICEEG recordings. The primary outcome was frequency of disabling seizures at last 3‐month follow‐up after MTL resection compared to seizure frequency 3 months before MTL resection.ResultsWe identified 157 patients treated with the RNS System with bilateral MTL leads due to presumed bitemporal epilepsy. Twenty‐five patients (16%) subsequently had an MTL resection informed by chronic ambulatory ICEEG (mean = 42 months ICEEG); follow‐up was available for 24 patients. After MTL resection, the median reduction in disabling seizures at last follow‐up was 100% (mean: 94%; range: 50%‐100%). Nine patients (38%) had exclusively unilateral electrographic seizures recorded by chronic ambulatory ICEEG and all were seizure‐free at last follow‐up after MTL resection; eight of nine continued RNS System treatment. Fifteen patients (62%) had bilateral MTL electrographic seizures, had an MTL resection on the more active side, continued RNS System treatment, and achieved a median clinical seizure reduction of 100% (mean: 90%; range: 50%‐100%) at last follow‐up, with eight of fifteen seizure‐free. For those with more than 1 year of follow‐up (N = 21), 15 patients (71%) were seizure‐free during the most recent year, including all eight patients with unilateral onsets and 7 of 13 patients (54%) with bilateral onsets.SignificanceChronic ambulatory ICEEG data provide information about lateralization of MTL seizures and can identify additional patients who may benefit from MTL resection.

Supraventricular Tachycardia and Postural Orthostatic Tachycardia Syndrome Overlap: A Retrospective Study.

Postural orthostatic tachycardia syndrome (POTS) and supraventricular tachycardia (SVT) are disease states with distinctive features but overlapping clinical manifestations. Currently, studies on the presence of underlying SVT in patients with POTS are lacking. This retrospective study analyzed 64 patients [mean age: 43 years; 41 (61%) women] who had a POTS diagnosis and were found to have concomitant SVT during rhythm monitoring from September 1, 2013 to September 30, 2019 at our Syncope and Autonomic Disorders Clinic. The outcomes assessed were changes in disease severity, frequency of symptoms, heart rate, and blood pressure between before and after SVT ablation. The most frequent types of SVT noted on the electrophysiologic study were atrioventricular nodal reentrant tachycardia (57.81%), atrial flutter (29.68%), atrioventricular reentrant tachycardia (9.37%), atrial tachycardia (1.56%), and junctional tachycardia (1.56%). After SVT ablation, all 64 patients experienced an improvement in symptoms. Palpitations and lightheadedness experienced the most improvement after the procedure (72% vs. 31%; p < 0.001 and 63% vs. 22%; p < 0.001, respectively). There was a significant improvement in the resting heart rate (81.1 ± 12.8 vs. 75.8 ± 15.6 bpm; p < 0.002), but the orthostatic tachycardia on standing persisted (93.6 ± 16.5 vs. 77.3 ± 19.8 bpm; p = 0.14). Underlying SVT in patients with POTS can be missed easily. A strong suspicion and long-term ambulatory cardiac rhythm monitoring can help in diagnosing the condition.

Critical analysis of electrohysterographic methods for continuous monitoring of intrauterine pressure.

Monitoring the progression of uterine activity provides important prognostic information during pregnancy and delivery. Currently, uterine activity monitoring relies on direct or indirect mechanical measurements of intrauterine pressure (IUP), which are unsuitable for continuous long-term observation. The electrohysterogram (EHG) provides a non-invasive alternative to the existing methods and is suitable for long-term ambulatory use. Several published state-of-the-art methods for EHG-based IUP estimation are here discussed, analyzed, optimized, and compared. By means of parameter space exploration, key parameters of the methods are evaluated for their relevance and optimal values. We have optimized all methods towards higher IUP estimation accuracy and lower computational complexity. Their accuracy was compared with the gold standard accuracy of internally measured IUP. Their computational complexity was compared based on the required number of multiplications per second (MPS). Significant reductions in computational complexity have been obtained for all published algorithms, while improving IUP estimation accuracy. A correlation coefficient of 0.72 can be obtained using fewer than 120 MPS. We conclude that long-term ambulatory monitoring of uterine activity is possible using EHG-based methods. Furthermore, the choice of a base method for IUP estimation is less important than the correct selection of electrode positions, filter parameters, and postprocessing methods. The presented review of state-of-the-art methods and applied optimizations show that long-term ambulatory IUP monitoring is feasible using EHG measurements.

Left atrial strain for predicting atrial fibrillation in hypertrophic cardiomyopathy

Patients with hypertrophic cardiomyopathy (HCM) are prone to develop atrial fibrillation (AF) due to diastolic dysfunction and left atrial (LA) remodeling. According to guidelines, 48-hour ambulatory ECG is recommended every 12–24 months in stable patients and every 6–12 months in case of LA dimension ≥ 45 mm or if new palpitations. The aim of this study is to investigate the predictive value of LA speckle tracking in detecting paroxysmal atrial fibrillation in HCM patients with enlarged left atrium. We enrolled 62 patients with HCM in sinus rhythm and LA diameter ≥ 45 mm. All patients had previously underwent 24 or 48-hour ambulatory ECG in the last 12 months without detecting any paroxysmal AF. In this study, all our patients underwent standard echocardiographic with left ventricular (LV) and LA speckle tracking examination followed by 7-days long-term ambulatory ECG monitoring. Among 62 patients, 19 patients (30.6%) were diagnosed with asymptomatic paroxysmal AF detected by 7-days long-term ambulatory ECG recorder (group1). In the same conditions, no paroxysmal FA was detected in the remaining 43 patients (group2). Group 1 patients showed no differences from group 2 patients with regards to mean ages, BMI, LV dimensions and ejection fraction, E/e’ ratio and even in LA diameter. However, they had significantly impaired left atrial strain indices with significantly lower LA conduit strain 12.15 ± 3.7% vs.17.34 ± 4.08% ( P < 0.01), lower LA reservoir strain 24.78 ± 4.49% vs. 32.18 ± 5.6% ( P < 0.001) and lower LA contractile strain −12.02 ± 3.91% vs. −15.16 ± 4.12% ( P < 0.01). Among HCM patients with enlarged LA, those with impaired LA strain indices were prone to higher risk for paroxysmal FA. Thus, LA strain may provide additional functional information for predicting AF susceptibility and may lead in case of impaired indices to propose prolonged ECG monitoring in order to enhance the detection of undiagnosed AF.

Robust, real-time generic detector based on a multi-feature probabilistic method.

Robust, real-time event detection from physiological signals acquired during long-term ambulatory monitoring still represents a major challenge for highly-artifacted signals. In this paper, we propose an original and generic multi-feature probabilistic detector (MFPD) and apply it to real-time QRS complex detection under noisy conditions. The MFPD method calculates a binary Bayesian probability for each derived feature and makes a centralized fusion, using the Kullback-Leibler divergence. The method is evaluated on two ECG databases: 1) the MIT-BIH arrhythmia database from Physionet containing clean ECG signals, 2) a benchmark noisy database created by adding noise recordings of the MIT-BIH noise stress test database, also from Physionet, to the MIT-BIH arrhythmia database. Results are compared with a well-known wavelet-based detector, and two recently published detectors: one based on spatiotemporal characteristic of the QRS complex and the second, as the MFDP, based on feature calculations from the University of New South Wales detector (UNSW). For both benchmark Physionet databases, the proposed MFPD method achieves the lowest standard deviation in sensitivity and positive predictivity (+P) despite its online algorithm architecture. While the statistics are comparable for low-to mildly artifactual ECG signals, the MFPD outperforms reference methods for artifacted ECG with low SNR levels reaching 87.48 ± 14.21% in sensitivity and 89.39 ± 14.67% in +P as compared to 88.30 ± 17.66% and 86.06 ± 19.67% respectively from UNSW, the best performing reference method. With demonstrations on the extensively studied QRS detection problem, we consider that the proposed generic structure of the multi-feature probabilistic detector should offer promising perspectives for long-term monitoring applications for highly-artifacted signals.

Simulator of a Full Fetal Electrocardiogram Measurement Chain by Multichannel Capacitive Sensing

Long-term ambulatory monitoring of the fetal heart rate (fHR) can greatly increase insight into fetal well-being and reduce pregnancy risks. Unfortunately, the existing solutions using wet or dry electrodes are unsuitable for long-term fHR monitoring due to the use of a gel or direct skin contact. Capacitive electrodes can measure an electrocardiographic (ECG) signal through clothes and, therefore, are perfectly suitable for long-term monitoring of the fHR. However, capacitive fetal ECG (fECG) measurements are challenging due to the high sensitivity of capacitive sensors to motion artifacts (MAs) and the low amplitude of the fECG. This article aims at investigating the feasibility of fECG measurements using capacitive electrodes with dedicated postprocessing algorithms for signal-to-noise ratio (SNR) improvement and MA reduction. To this end, a novel simulator of the full measurement chain is realized that generates multichannel capacitive fECG data with artificial MAs and system noise. A dedicated blind source separation (BSS) algorithm is then employed for MA removal and fECG extraction. The extracted fECG is evaluated by SNR calculation and R-peak detection. Our results show that the BSS algorithm may extract the fECG signal from noisy capacitive data. In addition, lower system noise or higher number of channels may lead to better fECG extraction. A maximum increase of 0.5 dB in the SNR and decrease of 80.7 % in the R-peak detection error are observed with increased electrode number from 8 to 20. Our findings provide useful insights for the hardware design of a capacitive fECG measurement system.

Motion-Artifact Reduction in Capacitive Heart-Rate Measurements by Adaptive Filtering

Electrocardiography (ECG) measurements are esse- ntial components in clinical diagnosis and monitoring. Conventional ECG measurements produce discomfort to the patients due to the use of gel and direct skin contact. Capacitive electrodes can measure ECG signals through an isolation layer; they are especially suitable for long-term ambulatory monitoring. However, capacitive ECG measurements are severely affected by motion artifacts (MAs) due to variable coupling distance. Adaptive filtering has been widely used for MA reduction in ECG measurements. Unfortunately, a reference signal recorded by additional sensors is required for the existing adaptive-filtering methods, limiting their applicability in ambulatory settings. In this paper on capacitive ECG recordings, a novel adaptive-filtering method is proposed for MA removal, where the reference signal is extracted from the power-line interference (PLI). PLI is particularly evident in a capacitive ECG due to unbalanced coupling capacitance. Along with MAs, electrode movement causes variations in the PLI amplitude. By demodulating the PLI, a reference signal reflecting variations in the coupling capacitance can be extracted for adaptive MA removal. The proposed method was evaluated both by simulations and real data and compared with the acceleration-based adaptive-filtering method. Comparable or higher ECG signal-to-noise ratio was achieved by the proposed method with a computational cost of $79~\mu \text{s}$ /iteration, indicating effective MA removal. The proposed method may, therefore, lead to improved analysis of capacitive ECG signals in ambulatory settings.

Analysis of Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection.

Concealable, miniaturized electroencephalography (mini-EEG) recording devices are crucial enablers toward long-term ambulatory EEG monitoring. However, the resulting miniaturization limits the inter-electrode distance and the scalp area that can be covered by a single device. The concept of wireless EEG sensor networks (WESNs) attempts to overcome this limitation by placing a multitude of these mini-EEG devices at various scalp locations. We investigate whether optimizing the WESN topology can compensate for miniaturization effects in an auditory attention detection (AAD) paradigm. Starting from standard full-cap high-density EEG data, we emulate several candidate mini-EEG sensor nodes that locally collect EEG data with embedded electrodes separated by short distances. We propose a greedy group-utility based channel selection strategy to select a subset of these candidate nodes to form a WESN. We compare the AAD performance of this WESN with the performance obtained using long-distance EEG recordings. The AAD performance using short-distance EEG measurements is comparable to using an equal number of long-distance EEG measurements if, in both cases, the optimal electrode positions are selected. A significant increase in performance was found when using nodes with three electrodes over nodes with two electrodes. When the nodes are optimally placed, WESNs do not significantly suffer from EEG miniaturization effects in the case of AAD. WESN-like platforms allow us to achieve similar AAD performance as with long-distance EEG recordings while adhering to the stringent miniaturization constraints for ambulatory EEG. Their applicability in an AAD task is important for the design of neuro-steered auditory prostheses.