Electroencephalography complexity in infantile spasms and its association with treatment response

Chu, Yen-Ju; Chang, Chi-Feng; Weng, Wen‐Chin; Fan, Pi‐Chuan; Shieh, Jiann-Shing; Lee, Wang-Tso

doi:10.1016/j.clinph.2020.12.006

Cited by 17 publications

(19 citation statements)

References 36 publications

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“…Total EEG complexity was measured before and after subsequent ACTH treatment, as well as after 6 months of follow-up. The total EEG complexity following 14-days of ACTH therapy was not significantly different between ACTH responders and non-responders; however, at 6 months of follow-up, ACTH responders exhibited higher total EEG complexity than ACTH non-responders ( 44 ).…”

Section: Computational Analysismentioning

confidence: 72%

“…The permutation entropy was also generally lower in infants with IS, particularly in the delta and theta bands during wakefulness and the delta and beta bands during sleep ( 41 ). Another entropy measure, called multiscale entropy, suggested that the total EEG complexity in IS patients was significantly lower than in healthy children ( 44 ).…”

Section: Computational Analysismentioning

confidence: 99%

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EEG biomarkers for the diagnosis and treatment of infantile spasms

et al. 2022

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Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.

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Section: Computational Analysismentioning

confidence: 72%

Section: Computational Analysismentioning

confidence: 99%

EEG biomarkers for the diagnosis and treatment of infantile spasms

et al. 2022

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“…However, when more or heavy stresses are added on the biological system, it cannot digest all these stresses, so the complexity is then reduced. That is why many papers [10,12,13,16,[33][34][35] have always shown loss of complexity or de-complexification during aging and diseases. In this study, we investigated the early stages of disease (e.g., our PD patients), consequently, biological systems tended to increase the complexity to combat the disease or stress for survival.…”

Section: Discussionmentioning

confidence: 99%

“…It measures the structural richness of information over multiple temporal scales. Because traditional entropy measures quantify only the regularity of time series on a single scale, MSE can measure the complexity of the system [11][12][13][14][15]. However, there is no direct relation between regularity and complexity.…”

Section: Introductionmentioning

confidence: 99%

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Hsieh

Abbod

2021

Electronics

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Parkinson’s disease (PD) is a type of neurodegenerative diseases. PD influences gait in many aspects: reduced gait speed and step length, increased axial rigidity, and impaired rhythmicity. Gait-related data used in this study are from PhysioNet. Twenty-one PD patients and five healthy controls (CO) were sorted into four groups: PD without task (PDw), PD with dual task (PDd), control without task (COw), and control with dual task (COd). Since dual task actions are attention demanding, either gait or cognitive function may be affected. To quantify the used walking data, eight pressure sensors installed in each insole are used to measure the vertical ground reaction force. Thus, quantitative measurement analysis is performed utilizing multiscale entropy (MSE) and complexity index (CI) to analyze and differentiate between the ground reaction force of the four different groups. Results show that the CI of patients with PD is higher than that of CO and 11 of the sensor signals are statistically significant (p < 0.05). The COd group has larger CI values at the beginning (p = 0.021) but they get lower at the end of the test (p = 0.000) compared to that in the COw group. The end-of-test CI for the PDw group is lower in one of the feet sensor signals, and in the right total ground reaction force compared to the PDd group counterparts. In conclusion, when people start to adjust their gait due to pathology or stress, CI may increase first and reach a peak, but it decreases afterward when stress or pathology is further increased.

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“…A decreased MSE CI reflects reduced system complexity. The analysis of MSE has been adopted to investigate the complexity of human physiological dynamical systems such as heart rate [ 5 , 7 , 8 , 9 , 10 , 11 , 12 ], electroencephalogram (EEG) [ 13 , 14 , 15 , 16 , 17 , 18 ], gait [ 19 ], posture [ 6 , 20 , 21 ], and others [ 22 ] in various kinds of patients. These studies have observed a common phenomenon, that reduced complexity for the studied systems is related to disease or aging.…”

Section: Introductionmentioning

confidence: 99%

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis

Chen

Shen

Peng

et al. 2022

Entropy

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Studying heart rate dynamics would help understand the effects caused by a hyperkinetic heart in patients with hyperthyroidism. By using a multiscale entropy (MSE) analysis of heart rate dynamics derived from one-channel electrocardiogram recording, we aimed to compare the system complexity of heart rate dynamics between hyperthyroid patients and control subjects. A decreased MSE complexity index (CI) computed from MSE analysis reflects reduced system complexity. Compared with the control subjects (n = 37), the hyperthyroid patients (n = 37) revealed a significant decrease (p < 0.001) in MSE CI (hyperthyroid patients 10.21 ± 0.37 versus control subjects 14.08 ± 0.21), sample entropy for each scale factor (from 1 to 9), and high frequency power (HF) as well as a significant increase (p < 0.001) in low frequency power (LF) in normalized units (LF%) and ratio of LF to HF (LF/HF). In conclusion, besides cardiac autonomic dysfunction, the system complexity of heart rate dynamics is reduced in hyperthyroidism. This finding implies that the adaptability of the heart rate regulating system is impaired in hyperthyroid patients. Additionally, it might explain the exercise intolerance experienced by hyperthyroid patients. In addition, hyperthyroid patients and control subjects could be distinguished by the MSE CI computed from MSE analysis of heart rate dynamics.

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Electroencephalography complexity in infantile spasms and its association with treatment response

Cited by 17 publications

References 36 publications

EEG biomarkers for the diagnosis and treatment of infantile spasms

EEG biomarkers for the diagnosis and treatment of infantile spasms

Gait Analyses of Parkinson’s Disease Patients Using Multiscale Entropy

Reduced System Complexity of Heart Rate Dynamics in Patients with Hyperthyroidism: A Multiscale Entropy Analysis

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