An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation
Aim: Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude S...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Resuscitation Plus |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666520424000237 |
| _version_ | 1797242942337843200 |
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| author | Yuanshan Liu Tianen Zhou Qiyu Yang Yujing Lu Zhengfei Yang Jun Jiang |
| author_facet | Yuanshan Liu Tianen Zhou Qiyu Yang Yujing Lu Zhengfei Yang Jun Jiang |
| author_sort | Yuanshan Liu |
| collection | DOAJ |
| description | Aim: Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment. Methods: A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods. Result: Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001). Conclusion: In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice. |
| first_indexed | 2024-03-08T03:29:09Z |
| format | Article |
| id | doaj.art-3c98b2bf94e9415e970151edd9bf6f66 |
| institution | Directory Open Access Journal |
| issn | 2666-5204 |
| language | English |
| last_indexed | 2024-04-24T18:47:14Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Resuscitation Plus |
| spelling | doaj.art-3c98b2bf94e9415e970151edd9bf6f662024-03-27T04:53:07ZengElsevierResuscitation Plus2666-52042024-03-0117100572An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillationYuanshan Liu0Tianen Zhou1Qiyu Yang2Yujing Lu3Zhengfei Yang4Jun Jiang5Department of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, ChinaDepartment of Emergency, the First People’s Hospital of Foshan, Foshan, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou, ChinaDepartment of Emergency, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China; Corresponding authors at: Sun Yat-sen Memorial Hospital, Sun Yat-sen University 107 Yan Jiang Xi Road, Guangzhou 510120, China (Zhengfei Yang); Department of Emergency, the First People’s Hospital of Foshan, No. 81, North of Lingnan Avenue, Foshan, China (Jun Jiang).Department of Emergency, the First People’s Hospital of Foshan, Foshan, China; Corresponding authors at: Sun Yat-sen Memorial Hospital, Sun Yat-sen University 107 Yan Jiang Xi Road, Guangzhou 510120, China (Zhengfei Yang); Department of Emergency, the First People’s Hospital of Foshan, No. 81, North of Lingnan Avenue, Foshan, China (Jun Jiang).Aim: Spectral Flux (SF), which is based on common algorithms in the audio processing field, was applied to quantitatively analyze ECG signals to optimize the timing of defibrillation. With the aim of proving the performance in optimizing the timing of defibrillation, SF was compared with Amplitude Spectrum Area (AMSA) in a porcine model of ventricular fibrillation (VF) in a retrospective analysis experiment. Methods: A total of 56 male domestic pigs, weighing 40 ± 5 kg, were induced to undergo VF. Animals were then left untreated for 10 min, and after 6 min of cardiopulmonary resuscitation (CPR) defibrillation was performed. The respective SF and AMSA values were calculated every minute during VF and CPR. Comparisons were made through receiver operating characteristic (ROC) curves, one-way analyses of variance (one-way ANOVA), and scatterplots for the successful initial defibrillation sample (positive samples, Group R) and the failed initial defibrillation sample (negative samples, Group N) to illustrate the performance in optimizing the timing of defibrillation for the AMSA and SF methods. Result: Values of SF and AMSA gradually decreased during the 10 min VF period and increased in during the 6 min CPR period. The scatterplots showed that both metrics had the ability to distinguish positive and negative samples (p < .001). Meanwhile, ROC curves showed that SF (area under the curve, AUC = 0.798, p < .001) had the same ability as AMSA (AUC = 0.737, p < .001) to predict the successful defibrillation (Z = 1.35, p = 0.177). Moreover, when comparing the values for AMSA and SF between the successful initial defibrillation samples (Group R) and the failed initial defibrillation samples (Group N), the results showed that the values of both AMSA and SF in Group R were significantly higher than those in Group N (p < .001). Conclusion: In the present study, SF method had the same ability as AMSA to predict successful defibrillation with significantly higher values in cases of successful defibrillation than the instances in which defibrillation failed. Additionally, SF method might be more stable than AMSA for filtering out the higher frequency interference signals due to the narrower frequency range and had higher specificity and predictive accuracy than AMSA. So SF method had high clinical potential to optimize the timing of defibrillation. Nevertheless, further animal and clinical studies are still needed to confirm the effectiveness and practicality of SF as a predictive module for defibrillators in clinical practice.http://www.sciencedirect.com/science/article/pii/S2666520424000237Spectral FluxAmplitude Spectrum AreaECG signalsDefibrillation |
| spellingShingle | Yuanshan Liu Tianen Zhou Qiyu Yang Yujing Lu Zhengfei Yang Jun Jiang An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation Resuscitation Plus Spectral Flux Amplitude Spectrum Area ECG signals Defibrillation |
| title | An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| title_full | An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| title_fullStr | An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| title_full_unstemmed | An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| title_short | An acoustic method (Spectral Flux) to analyze ECG signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| title_sort | acoustic method spectral flux to analyze ecg signals for optimizing timing for defibrillation in a porcine model of ventricular fibrillation |
| topic | Spectral Flux Amplitude Spectrum Area ECG signals Defibrillation |
| url | http://www.sciencedirect.com/science/article/pii/S2666520424000237 |
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