Estimation of cerebral blood flow velocity during breath-hold challenge using artificial neural networks

The effect of untreated Obstructive Sleep Apnoea (OSA) on cerebral haemodynamics and CA impairment is an active field of research interest. A breath-hold challenge is usually used in clinical and research settings to simulate cardiovascular and cerebrovascular changes that mimic OSA events. This work utilises temporal arterial oxygen saturation (SpO₂) and photoplethysmography (PPG) signals to estimate the temporal cerebral blood flow velocity (CBFv) waveform. Measurements of CBFv, SpO₂, and PPG, were acquired concurrently from volunteers performing two different protocols of breath-hold challenge in the supine position. Past values of the SpO₂ and PPG signals were used to estimate the current values of CBFv using different permutations and topologies of supervised learning with shallow artificial neural networks (ANNs). The measurements from one protocol were used to train the ANNs and find the optimum topologies, which in turn were tested using the other protocol. Data collected from 10 normotensive, healthy subjects (four females, age 28.5 ± 6.1 years, Body Mass Index (BMI) 24.0 ± 4.7 kg/m²) were used in this study. The results show that different subjects have different optimum topologies for ANNs, thus indicating the effects of inter-subject variability on ANNs. Successfully reconstructed blind waveforms for the same subject group in the second protocol showed a reasonable accuracy of 60-80% estimation compared to the measured waveforms.

Temporal waveforms for SpO₂ and PPG contain adequate information to estimate the temporal CBFv waveform using ANNs.

Concurrent measurements of SpO₂ and PPG using pulse oximetry from the forehead and CBFv from the middle cerebral artery (MCA) using transcranial Doppler (TCD) were recorded from healthy, normotensive subjects performing a breath-hold challenge. The breath-hold challenge mimicked the cerebrovascular response to apnoea, and was recorded by measuring CBFv in MCA. Two protocols were used, each consisting of five breath-holding manoeuvres and differing in terms of the time between the five successive breath-holds. Using data from one protocol, several permutations of the temporal values of SpO₂ and PPG signals were used as inputs to different ANN topologies, in order to train and find the optimum model. The optimum model was evaluated using the data from the other protocol as a blind dataset.

Using the first protocol for training, optimum ANN configurations were found to be different for each subject, and accuracy of 75-87% was achieved. When these optimum ANN models were tested using the second protocol as a blind dataset, the accuracy achieved was around 60-80%.

A novel approach employing temporal records of SpO₂ and PPG can be used to estimate the CBFv waveform using ANNs with acceptable accuracy. Increases in the size and diversity of the population dataset and the use of features extracted from SpO₂ and PPG signals are needed for generalisation of the method and potential future clinical applications.