Reduction of false arrhythmia alarms using signal selection and machine learning

In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. In addition, patient safety is compromised because reaction time of the caregivers to true alarms is reduced.\u3cbr/\u3e\u3cbr/\u3eThe data for the algorithm development consisted of records of electrocardiogram (ECG), arterial blood pressure, and photoplethysmogram signals in which an alarm for either asystole, extreme bradycardia, extreme tachycardia, ventricular fibrillation or flutter, or ventricular tachycardia occurs. First, heart beats are extracted from every signal. Next, the algorithm selects the most reliable signal pair from the available signals by comparing how well the detected beats match between different signals based on ${{\text{F}}_{1}}$ -score and selecting the best match. From the selected signal pair, arrhythmia specific features, such as heart rate features and signal purity index are computed for the alarm classification. The classification is performed with five separate Random Forest models. In addition, information on the local noise level of the selected ECG lead is added to the classification. The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58

Decreasing the false alarm rate of arrhythmias in intensive care using a machine learning approach

We present a novel algorithm for classifying true and false alarms of five life-threatening arrhythmias in intensive care. This algorithm was entered in the PhysioNet/Computing in Cardiology Challenge 2015 Reducing False Arrhythmia Alarms in the ICU. The algorithm performs a binary classification of the alarms for a specified arrhythmia type by combining signal quality information and physiological features from multiple sources, such as electrocardiogram (ECG), photoplethysmogram (PPG), and arterial blood pressure (ABP). Signals were selected for feature computation by first assessing the quality for available signals. Random Forest classifiers were trained separately for every type of arrhythmia with arrhythmia-specific features. Hence, the complete algorithm leverages five different predictive models. Classification sensitivities of true alarms 75-99 % (average 93 %) on the training set with cross-validation and 22-100 %(average 90 %) on the unrevealed test set. Classification specificities on the training and test set were 76-94% (average 80%) and 75-100% (average 82%), respectively. The best performance was for extreme bradycardia, whereas the poorest results were for ventricular arrhythmias. The results are for the real-time category when only information prior to the alarm is considered. The final challenge score was 75.54

Force-interval relationships of the heart measured with photoplethysmography during atrial fibrillation

\u3cp\u3eForce-interval relationships (FIRs) of the heart represent the relationships between inter-beat intervals (IBIs) and strength of the ventricular contractions. These relationships are typically measured invasively and are altered from normal in heart failure (HF). An unobtrusive and continuous measurement of FIRs could be beneficial when HF and atrial fibrillation (AF) coexist in order to understand if AF causes progression of HF. We hypothesize that FIRs could be assessed during AF with IBIs and hemodynamic changes captured unobtrusively by photo-plethysmography (PPG) at the wrist. FIRs were assessed by using Spearman's rank correlation between the pulse onset change in the PPG waveform and either the preceding or pre-preceding IBIs (r \u3csub\u3epre\u3c/sub\u3e\ and r \u3csub\u3epre-pre\u3c/sub\u3e) in 5-minute segments. 32 patients (14 continuous AF, 18 no AF) were measured during the night with PPG and electrocardiography as a reference. The mean and standard deviation of r \u3csub\u3epre\u3c/sub\u3e were -0.25± 0.08 and 0.05± 0.12(p < 0.0001), and of r \u3csub\u3epre-pre\u3c/sub\u3e} 0.60± 0.09 and 0.16± 0.14 (p < 0.0001), during AF and sinus rhythm, respectively. Areas under the Receiver Operating Characteristics curve were 0.987 and 0.998, respectively. Thus, during AF the IBIs correlate with the beat-to-beat changes of blood volume measured with PPG, likely to indicate that FIRs can be measured unobtrusively with the PPG signal. \u3c/p\u3

Detecting episodes of brady- and tachycardia using photo-plethysmography at the wrist in free-living conditions

\u3cp\u3eDetecting episodes of bradycardia and tachycardia can help identifying the clinical relevance of common cardiac symptoms. This study aimed at investigating whether an unobtrusive wrist-wearable device equipped with a photo-plethysmographic (PPG) and acceleration sensor could be used to detect such rate abnormalities in free-living conditions. Twenty patients (M=55%, age: 67 ± 13 y) reporting cardiac symptoms were monitored for 24 hours in free-living conditions using a portable Holter ECG recorder. Simultaneously, a wrist-wearable device equipped with a PPG and acceleration sensor was used to measure heart rate and the mean inter-pulse-interval (IPI) in 5-sec epochs. ECG-derived inter-beat-intervals (IBI) were used as ground truth for determining episodes of bradycardia (>1200 ms) and tachycardia (<500 ms) during the monitoring period. According to the ECG, the duration of brady- and tachycardia and normal rate lasted a total of 766 min, 64 min, and 27024 min, respectively. Average IPI during bradycardia and tachycardia was 1310 ± 80 ms and 459 ± 37 ms, respectively. IPI data correctly identified episodes of bradycardia (Se: 85.0%, Sp: 99.4%) and tachycardia (Se: 89.1%, Sp: 99.9%). In conclusion, a wrist-wearable device equipped with a PPG sensor can accurately detect rate abnormalities such as brady- and tachycardia in free-living conditions.\u3c/p\u3

How accurately can we detect atrial fibrillation using Photoplethysmography data measured in daily life?

Photoplethysmography (PPG) is an unobtrusive measurement modality recently explored for the detection of atrial fibrillation (AF). When used in wrist-worn applications, PPG-monitoring can be used for long-term monitoring in daily life, which is beneficial when aiming to detect AF. The objective of this study was to investigate whether the performance of an AF detection model trained and tested on short measurements is generalizable to measurements in daily life. PPG, accelerometer, as well as reference ECG data were measured from 32 subjects (13 continuous AF, 19 no AF) in 24-hour monitoring in daily life. An AF detection model combining inter-pulse interval features was trained to classify AF or non-AF. Short measurements were obtained by selecting a 5-minute segment from each 24-hour recording and used for training the model. The accuracy was tested on both 5-minute segments and 24-hour data. Sensitivity, specificity, and accuracy of the model were 98.90%, 99.03%, and 98.98% with 5-minute data and 96.94%, 91.99%, and 93.91% with 24-hour data. False positive detections per patient worsened from being on average none during short recordings to (mean ± sd) 467 ± 328 in daily life. Thus, testing the AF detection models intended for long-term PPG-monitoring is essential with data from daily life in order to obtain a realistic estimate of the accuracy

Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions

\u3cp\u3eOBJECTIVE: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life.\u3c/p\u3e\u3cp\u3eAPPROACH: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement.\u3c/p\u3e\u3cp\u3eMAIN RESULTS: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa  =  0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa  =  0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG.\u3c/p\u3e\u3cp\u3eSIGNIFICANCE: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.\u3c/p\u3

Atrial fibrillation detection using photo-plethysmography and acceleration data at the wrist

Atrial fibrillation (AF) is a pathological cardiac\u3cbr/\u3econdition leading to increased risk for embolic stroke.\u3cbr/\u3eScreening for AF is challenging due to the paroxysmal and\u3cbr/\u3easymptomatic nature of the condition. We aimed to\u3cbr/\u3einvestigate whether an unobtrusive wrist-wearable device\u3cbr/\u3eequipped with a photo-plethysmographic (PPG) and\u3cbr/\u3eacceleration sensor could detect AF. Sixteen patients with\u3cbr/\u3esuspected AF were monitored for 24 hours in an outpatient\u3cbr/\u3esetting using a Holter ECG. Simultaneously, PPG and\u3cbr/\u3eacceleration data were collected at the wrist. PPG data\u3cbr/\u3ewas processed to determine the timing of heartbeats and\u3cbr/\u3einter-beat-interval (IBI). Wrist acceleration and PPG\u3cbr/\u3emorphology were used to discard IBIs in presence of\u3cbr/\u3emotion artefacts. An ECG validated first-order Markov\u3cbr/\u3emodel was used to assess the probability of irregular\u3cbr/\u3erhythm due to AF using PPG-derived IBIs. The AF\u3cbr/\u3edetection algorithm was compared with clinical\u3cbr/\u3eadjudications of AF episodes after review of the ECG\u3cbr/\u3erecords. AF detection was achieved with 97 ± 2%\u3cbr/\u3esensitivity and 99 ± 3% specificity. Due to motion\u3cbr/\u3eartefacts, the algorithm did not provide AF classification\u3cbr/\u3efor an average of 36 ± 9% of the 24 hours monitoring. We\u3cbr/\u3econcluded that a wrist-wearable device equipped with a\u3cbr/\u3ePPG and acceleration sensor can accurately detect rhythm\u3cbr/\u3eirregularities caused by AF in daily life

Validating features for atrial fibrillation detection from photoplethysmogram under hospital and free-living conditions

\u3cp\u3eAtrial fibrillation (AF) is the most commonly experienced sustained arrhythmia, and it increases risks of stroke and congestive heart failure. Unobtrusive wearable solutions with photoplethysmography (PPG) have been proposed for AF detection and the performance has been mainly evaluated for short-term measurements in controlled measurement settings. In this study, we evaluate the predictive value of features from PPG for AF detection under both hospital and free-living conditions. PPG from the wrist was measured from 18 patients before and after cardioversion and from 16 patients (4 with 100% AF) for 24 hours. Single-lead ECG and 24-hour Holter were used respectively as gold standards. Six PPG-based inter-beat interval (IBI) variability and irregularity features were computed in three different sliding time windows. Thresholds for AF classification for every individual feature were determined with the data from the hospital conditions and tested with the measurements from free-living conditions. Overall, the best classification results were obtained by using a 120-s window, pNN40 resulting as the best feature. On average, the sensitivity was higher in the hospital conditions (92.3% vs. 71.6%) and the specificity higher in the free-living conditions (60.7% vs. 84.9%). In conclusion, testing the classification perfomance in free-living conditions is essential to properly evaluate AF detection models.\u3c/p\u3