Detection of sleep apnoea from frequency analysis of heart rate variability

Drinnan, Michael; Allen, John; Langley, Philip; Murray, Alan

doi:10.1109/cic.2000.898506

Cited by 39 publications

(36 citation statements)

References 3 publications

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“…MCNAMES and FRASER, 2000;STEIN and DOMITROVICH, 2000;DRINNAN et al, 2000;BALLORA et al, 2000), as indicated with some keywords in Table 1.…”

Section: Algorithms For Apnoea Detectionmentioning

confidence: 99%

“…Six participants made use of spectral analysis of heart rate variability (JARVlS et al, 2000;DE CHAZAL et al, 2000;MCNAMES and FRASER, 2000;SHINAR et al, 2000;DRINNAN et al, 2000;SCHRADER et al, 2000). Two participants used the Hilbert transform to extract frequency information from the heart rate signal SCHRADER et al, 2000).…”

Section: Algorithms For Apnoea Detectionmentioning

confidence: 99%

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Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Penzel

McNames

Chazal³

et al. 2002

Med. Biol. Eng. Comput.

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380

199

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Sleep apnoea is a common disorder that is usually diagnosed through expensive studies conducted in sleep laboratories. Sleep apnoea is accompanied by a characteristic cyclic variation in heart rate or other changes in the waveform of the electrocardiogram (ECG). If sleep apnoea could be diagnosed using only the ECG, it could be possible to diagnose sleep apnoea automatically and inexpensively from ECG recordings acquired in the patient's home. This study had two parts. The first was to assess the ability of an overnight ECG recording to distinguish between patients with and without apnoea. The second was to assess whether the ECG could detect apnoea during each minute of the recording. An expert, who used additional physiological signals, assessed each of the recordings for apnoea. Research groups were invited to access data via the world-wide web and submit algorithm results to an international challenge linked to a conference. A training set of 35 recordings was made available for algorithm development, and results from a test set of 35 different recordings were made available for independent scoring. Thirteen algorithms were compared. The best algorithms made use of frequency-domain features to estimate changes in heart rate and the effect of respiration on the ECG waveform. Four of these algorithms achieved perfect scores of 100% in the first part of the study, and two achieved an accuracy of over 90% in the second part of the study.

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“…MCNAMES and FRASER, 2000;STEIN and DOMITROVICH, 2000;DRINNAN et al, 2000;BALLORA et al, 2000), as indicated with some keywords in Table 1.…”

Section: Algorithms For Apnoea Detectionmentioning

confidence: 99%

Section: Algorithms For Apnoea Detectionmentioning

confidence: 99%

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Penzel

McNames

Chazal³

et al. 2002

Med. Biol. Eng. Comput.

Self Cite

380

199

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“…In recent years, methods for SA detection based on HRV features have been investigated extensively [10] [11] [12] [13]. In contrast to previous work, this paper proposes to detect SA using delayed HRV features.…”

Section: Introductionmentioning

confidence: 86%

Sleep apnea detection using time-delayed heart rate variability

Nano

Long

Werth

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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“…In most of the subjects, the frequency spectrum immediately below the apnoea peak was relatively flat. The first visual analysis of the single computed spectrum from each subject led to a correct classification score of 28/30 (93%) [100]. Gregory et al suggested that long-lasting alterations existed in autonomic function in snoring subjects [101].…”

Section: Hrv and Sleepmentioning

confidence: 99%

Heart Rate Variability

Acharya

Joseph

Kannathal

et al.

Advances in Cardiac Signal Processing

167

173

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During normal sinus rhythm, the heart rate (HR) varies from beat to beat. Heart rate variability (HRV) results from the dynamic interplay between the multiple physiologic mechanisms that regulate the instantaneous HR. It is believed that Heart Rate Variability (HRV) will become as common as pulse, blood pressure or temperature in patient charts in the near future. In the last ten years more than 2000 published articles have been written about HRV. HRV has been used as a screening tool in many disease processes. Various medical disciplines are looking at HRV. In diabetes and heart disease it has been proven to be predictive of the likelihood of future events. Physiological Phenomenon of HRVThe origin of heartbeat is located in a sino-atrial (SA) node of the heart, where a group of specialized cells continuously generates an electrical impulse spreading all over the heart muscle through specialized pathways and creating process of heart muscle contraction well synchronized between both atriums and ventricles. The SA node generates such impulses about 100-120 times per minute at rest. However in healthy individual resting heart rate (HR) would never be that high. This is due to continuous control of the autonomic nervous system (ANS) over the output of SA node activity. Its net regulatory effect gives real HR. In healthy subject at rest it is ranging between 50 and 70 beats per minute.The autonomic nervous system is a part of the nervous system that nonvoluntarily controls all organs and systems of the body. As the other part of nervous system ANS has its central (nuclei located in brain stem) and peripheral components (afferent and efferent fibers and peripheral ganglia) accessing all internal organs. There are two branches of the autonomic nervous system -sympathetic and parasympathetic (vagal) nervous systems that always work as antagonists in their effect on target organs.

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Detection of sleep apnoea from frequency analysis of heart rate variability

Cited by 39 publications

References 3 publications

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings

Sleep apnea detection using time-delayed heart rate variability

Heart Rate Variability

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