Abstract P505: Real-world Data Validation Of A Novel P-wave Based Automatic Atrial Fibrillation Detection Algorithm

Background: Automatic ECG algorithms using only RR-variability in ECG to detect AF have shown high false positive rates. By including P-wave presence in the algorithm, research has shown that it can increase detection accuracy for AF. Methods: A novel RR- and P-wave based automatic detection algorithm implemented in the Coala Heart Monitor ("Coala", Coala Life AB, Sweden) was evaluated for detection accuracy by the comparison to blinded manual ECG interpretation based on real-world data. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where the algorithm had detected both irregular RR-rhythms and strong P-waves in either chest or thumb recording (non-AF episodes classified by algorithm as Category 12).The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown.The blinded recordings were each manually interpreted by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the detection algorithm to determine the number of additional false negative indications for AF as presented to the user. Results: The trained cardiologist manually interpreted 0 of the 100 recordings as AF. Manual interpretation showed that the novel automatic AF algorithm yielded 0 % False Negative error and 100 % Negative Predictive Value (NPV) for detection of AF. Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings). The 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves constituted 18% of all recordings with irregular RR-rhythms. Respiratory sinus arrhythmia was the single most prevalent condition and was found in 47% of irregular RR-rhythms with strong P-waves. Conclusion: The novel, P-wave based automatic ECG algorithm used in the Coala, showed a zero percent False Negative error rate for AF detection in ECG recordings with RR-variability but presence of P-waves, as compared to manual interpretation by a cardiologist.

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Abstract P504: Quality Validation Of Different ECG Leads, And An Automatic P-wave Detection Algorithm For Af, Based On Real-world Data

Circulation ◽

10.1161/circ.141.suppl_1.p504 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Magnus Samuelsson ◽

Åke Olsson

Keyword(s):

Respiratory Sinus Arrhythmia ◽

Real World ◽

Detection Algorithm ◽

P Wave ◽

Detection Accuracy ◽

Real World Data ◽

P Waves ◽

Sinus Arrhythmia ◽

Ecg Leads

Background: Single-lead ECG has shown in research to be affected by artifacts leading to lower diagnostic yield of Atrial Fibrillation (AF). Use of multiple ECG leads and algorithms for detection of AF has shown to increase detection accuracy and reduce false positives. Methods: A novel RR- and P-wave based automatic algorithm implemented in the 2-lead Coala Heart Monitor (Coala) was evaluated for detection accuracy and quality by the comparison to blinded manual ECG interpretation. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where both an irregular RR-rhythm and strong P-waves in either chest or thumb recording were detected.The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown. The blinded recordings were each manually interpreted and assessed for quality by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the cloud-based detection algorithm to determine the detection quality of the respective ECG leads. Results: Strong P-waves were detected more often in the chest ECG as compared to the thumb ECG (90 vs 32 recordings). The assessed quality of the ECG tracings was higher in the chest ECGs as compared to the thumb ECGs (4.61 vs 3.88). Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings), the 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves thus constituted 18% of all recordings with irregular RR-rhythms. Non-pathological rhythm (normal) was present in 84% of the recordings although all of these recordings contained irregular rhythm disturbances (respiratory sinus arrhythmia, PAC/PVC etc). Respiratory sinus arrhythmia was the single most prevalent condition and found in 47% of the recordings with irregular RR-rhythms with strong detected P-waves. Conclusion: The combination of chest and thumb ECG for detection of AF by an automatic P-wave based algorithm is shown to be more than 300% superior to thumb ECG alone with the majority of automatically detected P-waves and highest assessed ECG quality in the chest recordings.

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Real-world Data on Incidence, Clinical Characteristics and Outcome of Patients with Macrofocal Multiple Myeloma in the Novel Therapeutic Era: A study of the Greco-Israeli Collaborative Myeloma Working Group

Clinical Lymphoma Myeloma & Leukemia ◽

10.1016/j.clml.2019.09.318 ◽

2019 ◽

Vol 19 (10) ◽

pp. e190-e191

Author(s):

Eirini Katodritou ◽

Efstathios Kastritis ◽

Moshe Gatt ◽

Yael Cohen ◽

Irit Avivi ◽

...

Keyword(s):

Multiple Myeloma ◽

Real World ◽

Working Group ◽

Clinical Characteristics ◽

The Novel ◽

Real World Data ◽

World Data ◽

Novel Therapeutic

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Mining moving object data

Open Computer Science ◽

10.2478/s13537-012-0018-4 ◽

2012 ◽

Vol 2 (3) ◽

Cited By ~ 2

Author(s):

Jaroslav Zendulka ◽

Martin Pešek

Keyword(s):

Outlier Detection ◽

Real World ◽

Moving Objects ◽

Detection Algorithm ◽

Moving Object ◽

Location Based Services ◽

Data Sets ◽

Real World Data ◽

World Data ◽

Trajectory Outlier Detection

AbstractCurrently many devices provide information about moving objects and location-based services that accumulate a huge volume of moving object data, including trajectories. This paper deals with two useful analysis tasks — mining moving object patterns and trajectory outlier detection. We also present our experience with the TOP-EYE trajectory outlier detection algorithm, which we applied to two real-world data sets.

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Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research (Preprint)

JMIR Medical Informatics ◽

10.2196/27172 ◽

2021 ◽

Author(s):

Vendula Churová ◽

Roman Vyškovský ◽

Kateřina Maršálová ◽

David Kudláček ◽

Daniel Schwarz

Keyword(s):

Anomaly Detection ◽

Clinical Research ◽

Real World ◽

Detection Algorithm ◽

Real World Data ◽

World Data

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Lab*Fruits: A Rapid and Robust Outdoor Fruit Detection System Combining Bio-Inspired Features with One-Stage Deep Learning Networks

Sensors ◽

10.3390/s20010275 ◽

2020 ◽

Vol 20 (1) ◽

pp. 275 ◽

Cited By ~ 9

Author(s):

Raymond Kirk ◽

Grzegorz Cielniak ◽

Michael Mangan

Keyword(s):

Deep Learning ◽

Real World ◽

Model Complexity ◽

Detection Accuracy ◽

Learning Networks ◽

Real World Data ◽

Data Set ◽

World Data ◽

One Stage ◽

Controlled Conditions

Automation of agricultural processes requires systems that can accurately detect and classify produce in real industrial environments that include variation in fruit appearance due to illumination, occlusion, seasons, weather conditions, etc. In this paper we combine a visual processing approach inspired by colour-opponent theory in humans with recent advancements in one-stage deep learning networks to accurately, rapidly and robustly detect ripe soft fruits (strawberries) in real industrial settings and using standard (RGB) camera input. The resultant system was tested on an existent data-set captured in controlled conditions as well our new real-world data-set captured on a real strawberry farm over two months. We utilise F 1 score, the harmonic mean of precision and recall, to show our system matches the state-of-the-art detection accuracy ( F 1 : 0.793 vs. 0.799) in controlled conditions; has greater generalisation and robustness to variation of spatial parameters (camera viewpoint) in the real-world data-set ( F 1 : 0.744); and at a fraction of the computational cost allowing classification at almost 30fps. We propose that the L*a*b*Fruits system addresses some of the most pressing limitations of current fruit detection systems and is well-suited to application in areas such as yield forecasting and harvesting. Beyond the target application in agriculture this work also provides a proof-of-principle whereby increased performance is achieved through analysis of the domain data, capturing features at the input level rather than simply increasing model complexity.

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Anomaly Detection Algorithm for Real-World Data and Evidence in Clinical Research (Preprint)

10.2196/preprints.27172 ◽

2021 ◽

Author(s):

Vendula Churová ◽

Roman Vyškovský ◽

Kateřina Maršálová ◽

David Kudláček ◽

Daniel Schwarz

Keyword(s):

Anomaly Detection ◽

Clinical Research ◽

Data Quality ◽

Real World ◽

Detection Algorithm ◽

Data Detection ◽

Real World Data ◽

World Data ◽

Clinical Registries ◽

Anomalous Data

BACKGROUND Statistical analysis, which has become an integral part of evidence-based medicine, relies heavily on data quality that is of critical importance in modern clinical research. Input data are not only at risk of being falsified or fabricated, but also of being mishandled by investigators. OBJECTIVE The urgent need to assure the highest data quality possible has led to implementation of various auditing strategies designed to monitor clinical trials and detect errors of different origin that frequently occur in the field. METHODS An automatic anomaly detection algorithm based on machine learning that combines clustering with a series of distance metrics is presented. RESULTS The algorithm is built in a particular electronic data capture (EDC) system that stores real-world data in clinical registries. These data, together with newly generated, simulated anomalous data were utilized to evaluate the detection performance of this algorithm. CONCLUSIONS The experimental results demonstrate that the algorithm, which is universal, and as such may be implemented in other EDC systems, is capable of anomalous data detection with sensitivity exceeding 85%.

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