519 related articles for article (PubMed ID: 34387554)
1. Assessing Electrocardiogram and Respiratory Signal Quality of a Wearable Device (SensEcho): Semisupervised Machine Learning-Based Validation Study.
Xu H; Yan W; Lan K; Ma C; Wu D; Wu A; Yang Z; Wang J; Zang Y; Yan M; Zhang Z
JMIR Mhealth Uhealth; 2021 Aug; 9(8):e25415. PubMed ID: 34387554
[TBL] [Abstract][Full Text] [Related]
2. A signal quality assessment-based ECG waveform delineation method used for wearable monitoring systems.
Xie J; Peng L; Wei L; Gong Y; Zuo F; Wang J; Yin C; Li Y
Med Biol Eng Comput; 2021 Oct; 59(10):2073-2084. PubMed ID: 34432182
[TBL] [Abstract][Full Text] [Related]
3. [Study on the quality evaluation of electrocardiogram signal in wearable physiological monitoring system].
Han N; Lan K; Zhang Y; Wan T; Zhang Z; Cao D; Yan W
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2021 Feb; 38(1):131-137. PubMed ID: 33899437
[TBL] [Abstract][Full Text] [Related]
4. Reducing false alarm rates for critical arrhythmias using the arterial blood pressure waveform.
Aboukhalil A; Nielsen L; Saeed M; Mark RG; Clifford GD
J Biomed Inform; 2008 Jun; 41(3):442-51. PubMed ID: 18440873
[TBL] [Abstract][Full Text] [Related]
5. Pseudo anomalies enhanced deep support vector data description for electrocardiogram quality assessment.
Huang X; Zhang F; Fan H; Chang H; Zhou B; Li Z
Comput Biol Med; 2024 Mar; 170():107928. PubMed ID: 38228029
[TBL] [Abstract][Full Text] [Related]
6. Reduction of false arrhythmia alarms using signal selection and machine learning.
Eerikäinen LM; Vanschoren J; Rooijakkers MJ; Vullings R; Aarts RM
Physiol Meas; 2016 Aug; 37(8):1204-16. PubMed ID: 27454128
[TBL] [Abstract][Full Text] [Related]
7. Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study.
Kasaeyan Naeini E; Subramanian A; Calderon MD; Zheng K; Dutt N; Liljeberg P; Salantera S; Nelson AM; Rahmani AM
J Med Internet Res; 2021 May; 23(5):e25079. PubMed ID: 34047710
[TBL] [Abstract][Full Text] [Related]
8. Robust Heartbeat Classification for Wearable Single-Lead ECG via Extreme Gradient Boosting.
Zhu H; Zhao Y; Pan Y; Xie H; Wu F; Huan R
Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450733
[TBL] [Abstract][Full Text] [Related]
9. Normal and Abnormal Classification of Electrocardiogram: A Primary Screening Tool Kit.
Kirodiwal A; Jahnavi D; Dash A; Ghosh N; Patra A
Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():2001-2004. PubMed ID: 36086436
[TBL] [Abstract][Full Text] [Related]
10. Personalized seizure detection using logistic regression machine learning based on wearable ECG-monitoring device.
Jeppesen J; Christensen J; Johansen P; Beniczky S
Seizure; 2023 Apr; 107():155-161. PubMed ID: 37068328
[TBL] [Abstract][Full Text] [Related]
11. Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for the reduction of false alarms in ICUs.
Krasteva V; Jekova I; Leber R; Schmid R; Abächerli R
Physiol Meas; 2016 Aug; 37(8):1273-97. PubMed ID: 27454550
[TBL] [Abstract][Full Text] [Related]
12. A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment.
Satija U; Ramkumar B; Manikandan MS
IEEE Rev Biomed Eng; 2018; 11():36-52. PubMed ID: 29994590
[TBL] [Abstract][Full Text] [Related]
13. Short-term atrial fibrillation detection using electrocardiograms: A comparison of machine learning approaches.
Jahan MS; Mansourvar M; Puthusserypady S; Wiil UK; Peimankar A
Int J Med Inform; 2022 Jul; 163():104790. PubMed ID: 35552189
[TBL] [Abstract][Full Text] [Related]
14. SE-ResNet-ViT Hybrid Model for Noise Classification in Adhesive Patch-type Wearable Electrocardiographs.
Kim S; Lim J; Shin M; Jung S
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38082768
[TBL] [Abstract][Full Text] [Related]
15. Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation.
Böttcher S; Bruno E; Manyakov NV; Epitashvili N; Claes K; Glasstetter M; Thorpe S; Lees S; Dümpelmann M; Van Laerhoven K; Richardson MP; Schulze-Bonhage A;
JMIR Mhealth Uhealth; 2021 Nov; 9(11):e27674. PubMed ID: 34806993
[TBL] [Abstract][Full Text] [Related]
16. Machine learning detection of Atrial Fibrillation using wearable technology.
Lown M; Brown M; Brown C; Yue AM; Shah BN; Corbett SJ; Lewith G; Stuart B; Moore M; Little P
PLoS One; 2020; 15(1):e0227401. PubMed ID: 31978173
[TBL] [Abstract][Full Text] [Related]
17. A novel method to reduce false alarms in ECG diagnostic systems: capture and quantification of noisy signals.
Zhu W; Qiu L; Cai W; Yu J; Li D; Li W; Zhong J; Wang Y; Wang L
Physiol Meas; 2021 Jul; 42(7):. PubMed ID: 33878739
[No Abstract] [Full Text] [Related]
18. Respiratory Event Detection During Sleep Using Electrocardiogram and Respiratory Related Signals: Using Polysomnogram and Patch-Type Wearable Device Data.
Yeo M; Byun H; Lee J; Byun J; Rhee HY; Shin W; Yoon H
IEEE J Biomed Health Inform; 2022 Feb; 26(2):550-560. PubMed ID: 34288880
[TBL] [Abstract][Full Text] [Related]
19. Multi-stage SVM approach for cardiac arrhythmias detection in short single-lead ECG recorded by a wearable device.
Smisek R; Hejc J; Ronzhina M; Nemcova A; Marsanova L; Kolarova J; Smital L; Vitek M
Physiol Meas; 2018 Sep; 39(9):094003. PubMed ID: 30102239
[TBL] [Abstract][Full Text] [Related]
20. ECG signal quality during arrhythmia and its application to false alarm reduction.
Behar J; Oster J; Li Q; Clifford GD
IEEE Trans Biomed Eng; 2013 Jun; 60(6):1660-6. PubMed ID: 23335659
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]