| 1. |
- Abbaspour, S., et al.
(författare)
-
Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
- 2021
-
Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
-
Tidskriftsartikel (refereegranskat)abstract
- Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
|
|
| 2. |
- Tomasic, Ivan, et al.
(författare)
-
Comparison of publicly available beat detection algorithms performances on the ECGs obtained by a patch ECG device
- 2019
-
Ingår i: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532330984 ; , s. 275-278
-
Konferensbidrag (refereegranskat)abstract
- Eight ECG beat detection algorithms, from the PhysioNet's WFDB and Cardiovascular Signal toolboxes, were tested on twenty measurements, obtained by the Savvy patch ECG device, for their accuracy in beat detection. On each subject, one measurement is obtained while sitting and one while running. Each measurement lasted from thirty seconds to one minute. The measurements obtained while running were more challenging for all the algorithms, as most of them almost perfectly detected all the beats on the measurements obtained in sitting position. However, when applied on the measurements obtained while running, all the algorithms have performed with decreased accuracy. Considering overall percentage of the faulty detected peaks, the four best algorithms were jqrs, from the Cardiovascular Signal Toolbox, and ecgpuwave, gqrs, and wqrs, from the WFDB Toolbox, with percentages of faulty detected beats 1.7, 2.3, 2.9, and 3, respectively.
|
|
| 3. |
- Trobec, R., et al.
(författare)
-
Detection and Treatment of Atrial Irregular Rhythm with Body Gadgets and 35-channel ECG
- 2019
-
Ingår i: 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2019 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9789532330984 ; , s. 301-308
-
Konferensbidrag (refereegranskat)abstract
- The atrial irregular rhythm, often reflected in atrial fibrillation, undulation or flutter, is recognized as one of the major causes of brain stroke and entails an increased risk of thromboembolic events because it increases the likelihood of blood clots formation. Its early detection is becoming an increasingly important preventive measure. The paper presents a simple methodology for the detection of atrial irregular rhythm by ECG body gadget that can perform long-term measurements, e.g. several weeks or more. Multichannel ECG, on the body surface, gives a more detailed insight into the atrial activity in comparison to standard 12-lead ECG. The information from MECG is compared with single-channel patch ECG. The obtained results suggest that the proposed methodology could be useful in treatments of atrial irregular rhythm. One can obtain a reliable information about the time and duration of fibrillation events, or determine arrhythmic focuses and conductive pathways in heart atria, or study the effects of antiarrhythmic drugs on existing arrhythmias and on an eventual development of new types of arrhythmias.
|
|
| 4. |
- Gharehbaghi, Arash, et al.
(författare)
-
A Hybrid Machine Learning Method for Detecting Cardiac Ejection Murmurs
- 2018
-
Ingår i: EMBEC and NBC 2017. - Singapore : SPRINGER-VERLAG SINGAPORE PTE LTD. - 9789811051227 - 9789811051210 ; , s. 787-790
-
Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2%-88.8%, 83.4%-86.9% and 88.3%-90.0%, respectively.
|
|
| 5. |
|
|
| 6. |
- Kristoffersson, Annica, 1980-, et al.
(författare)
-
A Systematic Review on the Use of Wearable Body Sensors for Health Monitoring : A Qualitative Synthesis
- 2020
-
Ingår i: Sensors. - Basel, Switzerland : MDPI AG. - 1424-8220. ; 20:5
-
Forskningsöversikt (refereegranskat)abstract
- The use of wearable body sensors for health monitoring is a quickly growing field with the potential of offering a reliable means for clinical and remote health management. This includes both real-time monitoring and health trend monitoring with the aim to detect/predict health deterioration and also to act as a prevention tool. The aim of this systematic review was to provide a qualitative synthesis of studies using wearable body sensors for health monitoring. The synthesis and analysis have pointed out a number of shortcomings in prior research. Major shortcomings are demonstrated by the majority of the studies adopting an observational research design, too small sample sizes, poorly presented, and/or non-representative participant demographics (i.e., age, gender, patient/healthy). These aspects need to be considered in future research work.
|
|
| 7. |
|
|
| 8. |
- Loutfi, Amy, 1978-, et al.
(författare)
-
Ecare@home : A distributed research environment on semantic interoperability
- 2016
-
Ingår i: Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.. - Cham : Springer International Publishing. - 9783319512334 - 9783319512341 ; , s. 3-8
-
Konferensbidrag (refereegranskat)abstract
- This paper presents the motivation and challenges to developing semantic interoperability for an internet of things network that is used in the context of home based care. The paper describes a research environment which examines these challenges and illustrates the motivation through a scenario whereby a network of devices in the home is used to provide high-level information about elderly patients by leveraging from techniques in context awareness, automated reasoning, and configuration planning.
|
|
| 9. |
- Rattfält, Linda, 1979-, et al.
(författare)
-
Robust heartbeat detector based on weighted correlation and multichannel input : Implementation on the ECG recorded with textile electrodes
- 2013
-
Ingår i: International Journal of E-Health and Medical Communications. - : IGI Global. - 1947-315X .- 1947-3168. ; 4:1, s. 61-71
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to develop and evaluate a robust heartbeat detector for noisy electrocardiograms (ECGs) recorded with textile electrodes. The authors suggest a method based on weighted correlation in a multi-channel ECG to obtain a heartbeat detector. Signals were acquired during rest and at movements which simulate every day activities. From each recording a segment corresponding to a heartbeat was extracted and correlated with the whole signal. From the correlation data, heartbeat candidates were derived and weighted based on their variance similarity with the heartbeat model and previous heartbeats. Finally, the outputs of each channel were added to create the global output. The output was compared to the Pan Tompkins heartbeat detector. Results are promising for recordings at rest (sensitivity = 0.97, positive predictive value (PPV) = 0.97). For static muscle tension in the torso the results were much higher than the reference method (sensitivity = 0.77, PPV = 0.85). Corresponding values for the reference method were sensitivity = 0.96 and PPV = 0.95 at rest and sensitivity = 0.52 and PPV = 0.75 during muscle tension.
|
|
| 10. |
|
|
