Poincaré plot analysis of cerebral blood flow signals: Feature extraction and classification methods for apnea detection

Abstract

ObjectiveRheoencephalography is a simple and inexpensive technique for cerebral blood flow assessment, however, it is not used in clinical practice since its correlation to clinical conditions has not yet been extensively proved. The present study investigates the ability of Poincaré Plot descriptors from rheoencephalography signals to detect apneas in volunteers.MethodsA group of 16 subjects participated in the study. Rheoencephalography data from baseline and apnea periods were recorded and Poincaré Plot descriptors were extracted from the reconstructed attractors with different time lags (τ). Among the set of extracted features, those presenting significant differences between baseline and apnea recordings were used as inputs to four different classifiers to optimize the apnea detection.ResultsThree features showed significant differences between apnea and baseline signals: the Poincaré Plot ratio (SDratio), its correlation (R) and the Complex Correlation Measure (CCM). Those differences were optimized for time lags smaller than those recommended in previous works for other biomedical signals, all of them being lower than the threshold established by the position of the inflection point in the CCM curves. The classifier showing the best performance was the classification tree, with 81% accuracy and an area under the curve of the receiver operating characteristic of 0.927. This performance was obtained using a single input parameter, either SDratio or R.ConclusionsPoincaré Plot features extracted from the attractors of rheoencephalographic signals were able to track cerebral blood flow changes provoked by breath holding. Even though further validation with independent datasets is needed, those results suggest that nonlinear analysis of rheoencephalography might be a useful approach to assess the correlation of cerebral impedance with clinical changes.

Scalp-Mounted Electrical Impedance Tomography of Cerebral Hemodynamics

“…There has been much discussion in the REG literature, e.g. [ 9 ], [ 10 ], regarding its dependence on intra-cranial versus extra-cranial processes, with a consensus that, compared with bipolar measurements, tetrapolar REG signals have greater dependence on intra-cranial processes and can track CBF [ 11 ]. The EIT images reconstructed from the 509 tetrapolar REG measurements per frame recorded with fEITER Mk.…”

“…; ~ (10) The right hand side of (10) can be transformed backward to the difference of the discretisation model function of 1 ~+ i σ and that of σN with Jacobian error eJ , as shown in (11).…”

“…In developing the fast EIT system reported here, it was found that the trans-impedances recorded on the scalp undergo significant cyclical variation in synchronism with cardiac activity. This bio-impedance phenomenon, referred to as rheoencephalography (REG), has been widely studied in non-imaging mode [ 9 ], [ 10 ] and has shown the capability to track cerebral blood flow (CBF) [ 11 ]. Here, REG is explored further by EIT imaging in steady state.…”

Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

“…This work is based on a previously published dataset [ 56 ]. A group of 16 young healthy volunteers signed an informed consent for REG data recording during apnea and baseline periods.…”

Selección del tiempo de retardo en el gráfico de Poincaré: Medición de los niveles de sedación y analgesia con señales EEG

“…El teorema de Takens establece que el atractor de un sistema dinámico puede ser reconstruido como una representación en el espacio de estado para un tiempo de retardo especifico (lag) y una dimensión (m). A pesar de que el atractor de un sistema dado puede tener una alta dimensión, el análisis de los atractores reconstruidos en dos dimensiones se ha utilizado ampliamente para caracterizar señales biomédicas [17]. El grafico de Poincaré, es un caso particular del teorema de Takens, en donde se elige m = 2.…”