1,392 research outputs found

    Modulations of Heart Rate, ECG, and Cardio-Respiratory Coupling Observed in Polysomnography

    Get PDF
    The cardiac component of cardio-respiratory polysomnography is covered by ECG and heart rate recordings. However their evaluation is often underrepresented in summarizing reports. As complements to EEG, EOG, and EMG, these signals provide diagnostic information for autonomic nervous activity during sleep. This review presents major methodological developments in sleep research regarding heart rate, ECG and cardio-respiratory couplings in a chronological (historical) sequence. It presents physiological and pathophysiological insights related to sleep medicine obtained by new technical developments. Recorded nocturnal ECG facilitates conventional heart rate variability analysis, studies of cyclical variations of heart rate, and analysis of ECG waveform. In healthy adults, the autonomous nervous system is regulated in totally different ways during wakefulness, slow-wave sleep, and REM sleep. Analysis of beat-to-beat heart-rate variations with statistical methods enables us to estimate sleep stages based on the differences in autonomic nervous system regulation. Furthermore, up to some degree, it is possible to track transitions from wakefulness to sleep by analysis of heart-rate variations. ECG and heart rate analysis allow assessment of selected sleep disorders as well. Sleep disordered breathing can be detected reliably by studying cyclical variation of heart rate combined with respiration-modulated changes in ECG morphology (amplitude of R wave and T wave)

    Multimodal Signal Processing for Diagnosis of Cardiorespiratory Disorders

    Get PDF
    This thesis addresses the use of multimodal signal processing to develop algorithms for the automated processing of two cardiorespiratory disorders. The aim of the first application of this thesis was to reduce false alarm rate in an intensive care unit. The goal was to detect five critical arrhythmias using processing of multimodal signals including photoplethysmography, arterial blood pressure, Lead II and augmented right arm electrocardiogram (ECG). A hierarchical approach was used to process the signals as well as a custom signal processing technique for each arrhythmia type. Sleep disorders are a prevalent health issue, currently costly and inconvenient to diagnose, as they normally require an overnight hospital stay by the patient. In the second application of this project, we designed automated signal processing algorithms for the diagnosis of sleep apnoea with a main focus on the ECG signal processing. We estimated the ECG-derived respiratory (EDR) signal using different methods: QRS-complex area, principal component analysis (PCA) and kernel PCA. We proposed two algorithms (segmented PCA and approximated PCA) for EDR estimation to enable applying the PCA method to overnight recordings and rectify the computational issues and memory requirement. We compared the EDR information against the chest respiratory effort signals. The performance was evaluated using three automated machine learning algorithms of linear discriminant analysis (LDA), extreme learning machine (ELM) and support vector machine (SVM) on two databases: the MIT PhysioNet database and the St. Vincentโ€™s database. The results showed that the QRS area method for EDR estimation combined with the LDA classifier was the highest performing method and the EDR signals contain respiratory information useful for discriminating sleep apnoea. As a final step, heart rate variability (HRV) and cardiopulmonary coupling (CPC) features were extracted and combined with the EDR features and temporal optimisation techniques were applied. The cross-validation results of the minute-by-minute apnoea classification achieved an accuracy of 89%, a sensitivity of 90%, a specificity of 88%, and an AUC of 0.95 which is comparable to the best results reported in the literature

    Modulation of the Sympatho-Vagal Balance during Sleep: Frequency Domain Study of Heart Rate Variability and Respiration

    Get PDF
    Sleep is a complex state characterized by important changes in the autonomic modulation of the cardiovascular activity. Heart rate variability (HRV) greatly changes during different sleep stages, showing a predominant parasympathetic drive to the heart during non-rapid eye movement (NREM) sleep and an increased sympathetic activity during rapid eye movement (REM) sleep. Respiration undergoes important modifications as well, becoming deeper and more regular with deep sleep and shallower and more frequent during REM sleep. The aim of the present study is to assess both autonomic cardiac regulation and cardiopulmonary coupling variations during different sleep stages in healthy subjects, using spectral and cross-spectral analysis of the HRV and respiration signals. Polysomnographic sleep recordings were performed in 11 healthy women and the HRV signal and the respiration signal were obtained. The spectral and cross-spectral parameters of the HRV signal and of the respiration signal were computed at low frequency and at breathing frequency (high frequency, HF) during different sleep stages. Results attested a sympatho-vagal balance shift toward parasympathetic modulation during NREM sleep and toward sympathetic modulation during REM sleep. Spectral analysis of the HRV signal and of the respiration signal indicated a higher respiration regularity during deep sleep, and a higher parasympathetic drive was also confirmed by an increase in the coherence between the HRV and the respiration signal in the HF band during NREM sleep. Our findings about sleep stage-dependent variations in the HRV signal and in the respiratory activity are in line with previous evidences and confirm spectral analysis of the HRV and the respiration signal to be a suitable tool for investigating cardiac autonomic modulation and cardio-respiratory coupling during sleep

    A review of ECG-based diagnosis support systems for obstructive sleep apnea

    Get PDF
    Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy

    Cardiac autonomic activity during sleep in high-altitude resident children compared with lowland residents

    Get PDF
    Study Objectives We aimed to characterize heart-rate variability (HRV) during sleep in Andean children native to high altitude (HA) compared with age, gender, and genetic ancestry-similar low-altitude (LA) children. We hypothesized that the hypoxic burden of sleep at HA could induce variation in HRV. As children have otherwise healthy cardiovascular systems, such alterations could provide early markers of later cardiovascular disease. Methods Twenty-six LA (14F) and 18 HA (8F) children underwent a single night of attended polysomnography. Sleep parameters and HRV indices were measured. Linear mixed models were used to assess HRV differences across sleep stage and altitude group. Results All children showed marked fluctuations in HRV parameters across sleep stages, with higher vagal activity during nonrapid eye movement sleep and greater variability of the heart rate during rapid eye movement (REM). Moreover, HA children showed higher very low-frequency HRV in REM sleep and, after adjusting for heart rate, higher low-to-high frequency ratio in REM sleep compared with children living at lower altitude. Conclusions We confirmed previous findings of a stage-dependent modulation of HRV in Andean children living at both HA and LA. Moreover, we showed subtle alteration of HRV in sleep in HA children, with intriguing differences in the very low-frequency domain during REM sleep. Whether these differences are the results of an adaptation to high-altitude living, or an indirect effect of differences in oxyhemoglobin saturation remains unclear, and further research is required to address these questions

    Changes in Physiological Network Connectivity of Body System in Narcolepsy during REM Sleep: ๋ ˜ ์ˆ˜๋ฉด ์ค‘ ๊ธฐ๋ฉด ํ™˜์ž์˜ ์ธ์ฒด ์‹œ์Šคํ…œ ๋‚ด ์ƒ๋ฆฌํ•™์  ๋„คํŠธ์›Œํฌ ์—ฐ๊ฒฐ์„ฑ ๋ณ€ํ™”

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2022.2. ๋ฐ•๊ด‘์„.์—ฐ๊ตฌ ๋ฐฐ๊ฒฝ: ๊ธฐ๋ฉด์ฆ์€ ๋ณ‘๋ฆฌํ•™์  ์ฆ์ƒ์„ ์ˆ˜๋ฐ˜ํ•˜๋Š” ์ˆ˜๋ฉด ์งˆํ™˜์˜ ํ•˜๋‚˜๋กœ, ์•ผ๊ฐ„์— ์ถฉ๋ถ„ํ•œ ์ˆ˜๋ฉด์„ ์ทจํ–ˆ์Œ์—๋„ ์ฃผ๊ฐ„์˜ ๊ณผ๋„ํ•œ ์กธ๋ฆผ์ฆ, ๋ฌด๊ธฐ๋ ฅ์ฆ์˜ ์ฆ์ƒ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๊ธฐ๋ฉด์ฆ์€ ๋‘ ๊ฐ€์ง€์˜ ์œ ํ˜•์ด ์žˆ์œผ๋ฉฐ ์ด๋“ค์€ ํƒˆ๋ ฅ๋ฐœ์ž‘์„ ๋™๋ฐ˜ํ•œ 1์œ ํ˜• ๊ธฐ๋ฉด์ฆ๊ณผ ํƒˆ๋ ฅ๋ฐœ์ž‘์„ ๋™๋ฐ˜ํ•˜์ง€ ์•Š๋Š” 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ์œผ๋กœ ๊ตฌ๋ณ„๋œ๋‹ค. 1์œ ํ˜• ๊ธฐ๋ฉด์ฆ์˜ ์ง„๋‹จ ์ƒ์ฒด ์ง€ํ‘œ๋กœ์„œ ํžˆํฌํฌ๋ ˆํ‹ด์ด๋ผ๋Š” ์‹ ๊ฒฝ ๋ฌผ์งˆ์ด ์กด์žฌํ•˜์ง€๋งŒ, ๊ทธ์— ๋ฐ˜ํ•˜์—ฌ 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ์€ ์ ์ ˆํ•œ ์ƒ์ฒด ์ง€ํ‘œ๊ฐ€ ๋ถ€์žฌํ•˜์—ฌ 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ์˜ ๊ธฐ๋ฉด ์ฆ์ƒ ๋ฐ ์ธ๊ณผ๊ด€๊ณ„์˜ ํ™•์ธ์— ํ•œ๊ณ„๋ฅผ ์ง€๋‹ˆ๊ณ  ์žˆ๋‹ค. ์ด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ, ๋ณธ ์—ฐ๊ตฌ๋Š” 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ์˜ ์ƒˆ๋กœ์šด ์ƒ์ฒด ์ง€ํ‘œ์˜ ํƒ์ƒ‰์„ ๋ชฉํ‘œ๋กœ ํ•˜๋ฉฐ ์ด๋ฅผ ์œ„ํ•ด ์ธ์ฒด์˜ ์‹œ์Šคํ…œ์  ์—ฐ๊ฒฐ๋ง์˜ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” 30๋ช…์˜ ์ฐธ์—ฌ์ž (15๋ช…์˜ 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ ํ™˜์ž, 15๋ช…์˜ ์ •์ƒ ๋Œ€์กฐ๊ตฐ)๋ฅผ ๋Œ€์ƒ์œผ๋กœ, ์‹œ๊ฐ„ ์ง€์—ฐ ์•ˆ์ •์„ฑ์˜ ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์‹œ๊ฐ„์  ์ •๋ณด์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์—ฌ๋Ÿฌ ์ƒ์ฒด์‹ ํ˜ธ๋“ค์˜ ๊ด€๊ณ„์— ๋Œ€ํ•œ ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ๊ฐ ์ฐธ์—ฌ์ž์˜ ์•ผ๊ฐ„ ์ˆ˜๋ฉด๋‹ค์› ๊ฒ€์‚ฌ๋กœ๋ถ€ํ„ฐ ์–ป์€ 9๊ฐœ ์ƒ์ฒด์‹ ํ˜ธ๋“ค (๋‡ŒํŒŒ, ์‹ฌ์žฅ, ํ˜ธํก, ๊ทผ์œก๊ณผ ์•ˆ๊ตฌ์˜ ์›€์ง์ž„์œผ๋กœ ๋ถ€ํ„ฐ์˜ ์‹ ํ˜ธ)์˜ ์—ฐ๊ฒฐ์„ฑ ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ์ •๋Ÿ‰์  ๋ถ„์„์„ ์ง„ํ–‰ํ•˜์˜€์œผ๋ฉฐ, ํŠนํžˆ ์ˆ˜๋ฉด ๋‹จ๊ณ„์— ๋”ฐ๋ฅธ ๋„คํŠธ์›Œํฌ ์—ฐ๊ฒฐ์„ฑ์˜ ์ฐจ์ด๊ฐ€ ๋‘ ๊ทธ๋ฃน ์‚ฌ์ด์— ์–ด๋– ํ•œ ์˜ํ–ฅ๋ ฅ์„ ๋ฏธ์น˜๋ฉฐ ์ด๋“ค์ด ๊ธฐ๋ฉด์ฆ๊ณผ ์ •์ƒ๊ตฐ์„ ๊ตฌ๋ณ„ํ•˜๋Š” ์ž ์žฌ์  ์ƒ์ฒด ์ง€ํ‘œ๋กœ์„œ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์„์ง€์— ๋Œ€ํ•œ ๋ถ„์„์— ์ค‘์ ์„ ๋‘์—ˆ๋‹ค. ๊ทธ๋ฃน ๊ฐ„์˜ ์ฐจ์ด์— ๋Œ€ํ•œ ์ธ๊ณผ๊ด€๊ณ„์˜ ์กฐ์‚ฌ์™€ ํ•จ๊ป˜ ์ƒ์ฒด ์ง€ํ‘œ์˜ ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ์˜ ํ™•์ธ์„ ์œ„ํ•œ ์„œํฌํŠธ ๋ฒกํ„ฐ ๋จธ์‹  ๊ธฐ๋ฒ•์„ ์ ์šฉํ•œ ์กฐ์‚ฌ๋„ ํ•จ๊ป˜ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ: ๋ ˜ ์ˆ˜๋ฉด์—์„œ, ๊ธฐ๋ฉด ํ™˜์ž๊ตฐ์€ ์ •์ƒ ๋Œ€์กฐ๊ตฐ์— ๋น„๊ตํ•˜์—ฌ ๋” ๋งŽ์€ ๋„คํŠธ์›Œํฌ์˜ ์—ฐ๊ฒฐ์„ ๋ณด์˜€๋‹ค (๊ธฐ๋ฉด ํ™˜์ž ์—ฐ๊ฒฐ ์ˆ˜: 24.47 ยฑ 2.87, ๋Œ€์กฐ๊ตฐ ์—ฐ๊ฒฐ ์ˆ˜: 21.34 ยฑ 3.49; p = 0.022). ์ด๋Ÿฌํ•œ ์ฐจ์ด๋Š” ์—ฌ๋Ÿฌ ์—ฐ๊ฒฐ์˜ ์š”์†Œ๋“ค ์ค‘ ์›€์ง์ž„๊ณผ ๊ด€๋ จ๋œ ๊ธฐ๊ด€๊ณผ ์‹ฌ์žฅ ํ™œ๋™์—์„œ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚œ ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๋„คํŠธ์›Œํฌ์˜ ์—ฐ๊ฒฐ ๊ฐœ์ˆ˜์™€ ์œ ์˜๋ฏธํ•œ ์ฐจ์ด๋ฅผ ๋ณด์ด๋Š” ์ƒ์ฒด์‹ ํ˜ธ ์š”์†Œ์˜ ์ •๋ณด๋ฅผ ์ด์šฉํ•œ ์„œํฌํŠธ ๋ฒกํ„ฐ ๋จธ์‹  ๊ธฐ๋ฐ˜ ๋ถ„๋ฅ˜ ์„ฑ๋Šฅ์€ 0.93์˜ ๋ฏผ๊ฐ๋„, ํŠน์ด๋„, ์ •ํ™•๋„๋ฅผ ๊ฐ๊ฐ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ๊ฒฐ ๋ก : ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹œ๊ฐ„ ์ง€์—ฐ ์•ˆ์ •์„ฑ์— ๊ธฐ๋ฐ˜ํ•œ ๋„คํŠธ์›Œํฌ ์—ฐ๊ฒฐ์„ฑ์ด 2์œ ํ˜• ๊ธฐ๋ฉด์ฆ์„ ๋Œ€์กฐ๊ตฐ๊ณผ ๋ถ„๋ฅ˜ํ•˜๋Š” ๋ฐ์— ์žˆ์–ด ์œ ์šฉํ•œ ์ƒ์ฒด์ง€ํ‘œ๋กœ ์ด์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์ด๋ฉฐ, ๋‚˜์•„๊ฐ€ ์ธ์ฒด์˜ ์‹œ์Šคํ…œ์  ๋„คํŠธ์›Œํฌ์— ๋Œ€ํ•œ ๋ถ„์„์„ ํ†ตํ•ด ์ฐจ์ด์— ๋Œ€ํ•œ ์ธ๊ณผ๊ด€๊ณ„ ๋ถ„์„ ๋ฐ ์ •๋Ÿ‰์  ์ ‘๊ทผ์ด ๊ฐ€๋Šฅํ•จ์„ ๋ณด์—ฌ์ค€๋‹ค.Background: Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the bodyโ€™s systemic networks. Method: Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine. Result: In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ยฑ 2.87, control connections: 21.34 ยฑ 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively. Conclusion: Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.Chapter 1. Introduction 1 1.1. Narcolepsy 1 1.2. Physiological interactions in body system 3 1.3. Connectivity with time delay stability 5 1.4. Dissertation Outline 7 Chapter 2. Material and Methods 9 2.1. Participants 9 2.2. PSG recording and data 12 2.3. Data processing 14 2.4. Time delay cross-correleation 17 2.5. TDS methods 20 2.6. Threshold tuning 22 2.7. Test-retest reproducibility 25 2.8. Brain and peripheral connections 26 2.9. Effect of brain-brain connections according to brain areas 27 2.10. Feature significance analysis 28 2.11. Network directionality with correlation 29 2.12. Verifications of network connectivity as classifier 30 2.13. Classification with support vector machine 31 Chater 3. Results and Discussion 32 3.1. Results 32 3.1.1. Network connections between narcolepsy and control groups 32 3.1.2. Test-retest analysis for reproducibility 35 3.1.3. Significant feature identification 36 3.1.4. Effect of brain-brain connections according to brain areas 39 3.1.5. Network directionality with correlation 44 3.1.6. Performance comparison between unimodal biosignal and connectivity 46 3.1.7. Classification performance with SVM 49 3.2. Discussion 51 3.2.1. Differences between patients with narcolepsy and healthy controls 51 3.2.2. Analysis of nervous system with HRV 52 3.2.3. Causalities in network connections 55 3.2.4. Effect of brain-brain connections 58 3.2.5. Network connectivity as a biomarker and prospective utility 59 Limitations 61 References 63 ๊ตญ๋ฌธ์ดˆ๋ก 72์„

    Cardio-Respiratory Coordination Increases during Sleep Apnea

    Get PDF
    Funding: MR, NW, AM, TP and JK acknowledge financial support from RI2916/2-1, WE2834/5-1, PE628/4-1, and KU837/23-1 (Deutsche Forschungsgemeinschaft). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

    The Different Facets of Heart Rate Variability in Obstructive Sleep Apnea

    Get PDF
    Obstructive sleep apnea (OSA), a heterogeneous and multifactorial sleep related breathing disorder with high prevalence, is a recognized risk factor for cardiovascular morbidity and mortality. Autonomic dysfunction leads to adverse cardiovascular outcomes in diverse pathways. Heart rate is a complex physiological process involving neurovisceral networks and relative regulatory mechanisms such as thermoregulation, renin-angiotensin-aldosterone mechanisms, and metabolic mechanisms. Heart rate variability (HRV) is considered as a reliable and non-invasive measure of autonomic modulation response and adaptation to endogenous and exogenous stimuli. HRV measures may add a new dimension to help understand the interplay between cardiac and nervous system involvement in OSA. The aim of this review is to introduce the various applications of HRV in different aspects of OSA to examine the impaired neuro-cardiac modulation. More specifically, the topics covered include: HRV time windows, sleep staging, arousal, sleepiness, hypoxia, mental illness, and mortality and morbidity. All of these aspects show pathways in the clinical implementation of HRV to screen, diagnose, classify, and predict patients as a reasonable and more convenient alternative to current measures.Peer Reviewe

    Neurologic Issues in Patients Receiving Extracorporeal Membrane Oxygenation Support

    Get PDF
    Extracorporeal membrane oxygenation (ECMO) is a well-established therapy for patients experiencing acute severe cardiac and/or respiratory failure. Unfortunately, despite noteworthy improvements in patient selection, technology, and multidisciplinary team management, significant complications are still common. The most dramatic and potentially severe complications are neurologic. However, the incidence of neurologic complications (i.e. embolic stroke, intracerebral hemorrhage, seizures, and anoxic injuries) has not been completely defined. Unfortunately, brain death and neurologic injuries are significant causes of morbidity and mortality for patients requiring an ECMO support. Critical to the management of patients requiring ECMO is a broader understanding of neurologic monitoring along with the clinical assessment and management of neurologic events. It is important to evaluate and potentially intervene early in the event of a neurologic problem to minimize its clinical significance. Hopefully, with a better understanding of the pathophysiology, diagnostic and therapeutic tools, and prevention strategies, the true incidence of neurologic complications can be understood and minimized
    • โ€ฆ
    corecore