Driver drowsiness detection methods using EEG signals: a systematic review

Electroencephalography (EEG) is a complex signal that may require several years of training, advanced signal processing, and feature extraction methodologies to interpret correctly. Recently, many methods have been used to extract and classify EEG data. This study reviews 62 papers that used EEG signals to detect driver drowsiness, published between January 2018 and 2022. We extract trends and highlight interesting approaches from this large body of literature to inform future research and formulate recommendations. To find relevant papers published in scientific journals, conferences, and electronic preprint repositories, researchers searched major databases covering the domains of science and engineering. For each investigation, many data items about (1) the data, (2) the channels used, (3) the extraction and classification procedure, and (4) the outcomes were extracted. These items were then analyzed one by one to uncover trends. Our analysis reveals that the amount of EEG data used across studies varies. We saw that more than half the studies used simulation driving experimental. About 21% of the studies used support vector machine (SVM), while 19% used convolutional neural networks (CNN). Overall, we can conclude that drowsiness and fatigue impair driving performance, resulting in drivers who are more exposed to risky situations.

Heart sounds: Past, present, and future from a technological and clinical perspective - a systematic review

The high prevalence of cardiac diseases around the world has created a need for quick, easy and cost effective approaches to diagnose heart disease. The auscultation and interpretation of heart sounds using the stethoscope is relatively inexpensive, requires minimal to advanced training, and is widely available and easily carried by healthcare providers working in urban environments or medically underserved rural areas. Since René-Théophile-Hyacinthe Laennec's simple, monoaural design, the capabilities of modern-day, commercially available stethoscopes and stethoscope systems have radically advanced with the integration of electronic hardware and software tools, however these systems are largely confined to the metropolitan medical centers. The purpose of this paper is to review the history of stethoscopes, compare commercially available stethoscope products and analytical software, and discuss future directions. Our review includes a description of heart sounds and how modern software enables the measurement and analysis of time intervals, teaching auscultation, remote cardiac examination (telemedicine) and, more recently, spectrographic evaluation and electronic storage. The basic methodologies behind modern software algorithms and techniques for heart sound preprocessing, segmentation and classification are described to provide awareness.

On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines

Preterm birth is a global epidemic affecting millions of mothers across different ethnicities. The cause of the condition remains unknown but has recognised health-based implications, in addition to financial and economic ones. Machine Learning methods have enabled researchers to combine datasets using uterine contraction signals with various forms of prediction machines to improve awareness of the likelihood of premature births. This work investigates the feasibility of enhancing these prediction methods using physiological signals including uterine contractions, and foetal and maternal heart rate signals, for a population of south American women in active labour. As part of this work, the use of the Linear Series Decomposition Learner (LSDL) was seen to lead to an improvement in the prediction accuracies of all models, which included supervised and unsupervised learning models. The results from the supervised learning models showed high prediction metrics upon the physiological signals being pre-processed by the LSDL for all variations of the physiological signals. The unsupervised learning models showed good metrics for the partitioning of Preterm/Term labour patients from their uterine contraction signals but produced a comparatively lower set of results for the various kinds of heart rate signals investigated.

Identification of glottal instants using electroglottographic signal for vulnerable cases of voicing

Robust detection of glottal instants is essential for various speech and biomedical applications. Glottal closing and glottal opening are two crucial instants/epochs of a glottal cycle. The first-order derivative of the Electroglottographic (EGG) signal demonstrates important peaks at those locations for standard voicing, but the detection of glottal instants becomes erroneous when the peak to peak amplitude of the EGG signal is very low, irregular and unpredictable. In this work, a new efficient method is proposed for identification of glottal instants from the EGG signals including the segments of the signals where the signals are feeble with irregular periodicity. The overall accuracy of detection will be enhanced by identifying the glottal instants for the whole part of the signal including the vulnerable segments of signal. As the phase of a signal is uniform in nature, the phase information of the EGG signal has been explored to detect glottal instants accurately. Under low strength of the EGG signal, the proposed method remarkably has better performance compared to the existing instants detection methods and for pathological EGG signal, the detection accuracy of glottal instants is better than other existing methods.

Efficient implementation of LMS adaptive filter-based FECG extraction on an FPGA

In this Letter, the field programmable gate array (FPGA) implementation of a foetal heart rate (FHR) monitoring system is presented. The system comprises a preprocessing unit to remove various types of noise, followed by a foetal electrocardiogram (FECG) extraction unit and an FHR detection unit. To improve the precision and accuracy of the arithmetic operations, a floating-point unit is developed. A least mean squares algorithm-based adaptive filter (LMS-AF) is used for FECG extraction. Two different architectures, namely series and parallel, are proposed for the LMS-AF, with the series architecture targeting lower utilisation of hardware resources, and the parallel architecture enabling less convergence time and lower power consumption. The results show that it effectively detects the R peaks in the extracted FECG with a sensitivity of 95.74–100% and a specificity of 100%. The parallel architecture shows up to an 85.88% reduction in the convergence time for non-invasive FECG databases while the series architecture shows a 27.41% reduction in the number of flip flops used when compared with the existing FPGA implementations of various FECG extraction methods. It also shows an increase of 2–7.51% in accuracy when compared to previous works.

Explainable artificial intelligence for heart rate variability in ECG signal

Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features. Most of the proposed works lack the interpretation and understanding of the results obtained. Hence, the objective of this letter is to explore the features learned by the deep learning models. For the detection of the different types of tachycardia diseases, the authors used a transfer learning approach. In this method, the model is trained with one of the tachycardia diseases called atrial fibrillation and tested with other tachycardia diseases, such as ventricular fibrillation and sinus tachycardia. The analysis was done using different deep learning models, such as RNN, LSTM, GRU, CNN, and RSCNN. RNN achieved an accuracy of 96.47% for atrial fibrillation data set, 90.88% accuracy for CU-ventricular tachycardia data set, and also achieved an accuracy of 94.71, and 94.18% for MIT-BIH malignant ventricular ectopy database for ECG lead I and lead II, respectively. The RNN model could only achieve an accuracy of 23.73% for the sinus tachycardia data set. A similar trend is shown by other models. From the analysis, it was evident that even though tachycardia diseases have fast beat rhythm as their common feature, the model was not able to detect different types of tachycardia diseases. The deep learning model could only detect atrial fibrillation and ventricular fibrillation and failed in the case of sinus tachycardia. From the analysis, they were able to interpret that, along with the fast beat rhythm, the model has learned the absence of P-wave which is a common feature for ventricular fibrillation and atrial fibrillation but sinus tachycardia disease has an upright positive P-wave. The time-based analysis is conducted to find the time complexity of the models. The analysis conveyed that RNN and RSCNN models could achieve better performance with lesser time complexity.

Impact of varying diastolic pressure fitting technique for the reservoir-wave model on wave intensity analysis

The reservoir-wave model assumes that the measured arterial pressure is made of two components: reservoir and excess. The effect of the reservoir volume should be excluded to quantify the effects of forward and backward traveling waves on blood pressure. Whilst the validity of the reservoir-wave concept is still debated, there is no consensus on the best fitting method for the calculation of the reservoir pressure waveform. Therefore, the aim of this parametric study is to examine the effects of varying the fitting technique on the calculation of reservoir and excess components of pressure and velocity waveforms. Common carotid pressure and flow velocity were measured using applanation tonometry and doppler ultrasound, respectively, in 1037 healthy humans collected randomly from the Asklepios population, aged 35 to 55 years old. Different fitting techniques to the diastolic decay of the measured arterial pressure were used to determine the asymptotic pressure decay, which in turn was used to determine the reservoir pressure waveform. The corresponding wave speed was determined using the PU-loop method, and wave intensity parameters were calculated and compared. Different fitting methods resulted in significant changes in the shape of the reservoir pressure waveform; however, its peak and time integral remained constant in this study. Although peak and integral of excess pressure, velocity components and wave intensity changed significantly with changing the diastolic decay fitting method, wave speed was not substantially modified. We conclude that wave speed, peak reservoir pressure and its time integral are independent of the diastolic pressure decay fitting techniques examined in this study. Therefore, these parameters are considered more reliable diagnostic indicators than excess pressure and velocity which are more sensitive to fitting techniques.

Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering

A signal-piloted linear phase filtering tactic for removing baseline wander and power-line interference from the electrocardiogram (ECG) signals is suggested. The system is capable of adjusting its parameters by following the incoming signal variations. It renders the processing of lesser samples by inferior order filters. The applicability is demonstrated by using the MIT-BIH ECG database. The precision of the approach is also studied regarding the signal-to-noise ratio (SNR). Results showed that the proposed method achieves a 2.18-fold compression gain and notable computational efficiency over conventional counterpart while securing an analogous output SNR. A comparison of the designed solution is made with the contemporary empirical mode decomposition with Kalman filtering and eigenvalue decomposition based tactics. Results show that the suggested method performs better in terms of output SNR for the studied cases.

Blood pressure estimation system using human body communication-based electrocardiograph and photoplethysmography

In order to realise low-load cuffless and continuous blood pressure measurement in daily life, the authors developed a blood pressure estimation system combining human body communication-based wearable electrocardiograph and reflectance photoplethysmography. The principle is based on a relationship between the pulse arrive time and the systolic blood pressure. The pulse arrive time is the time period between the R-wave in electrocardiograph and peak of pulse wave. The greatest feature is the use of a human body communication-based electrocardiograph which can provide automatic synchronisation in time between the measured electrocardiograph and pulse wave signals to obtain the pulse arrive time so that no additional synchronisation circuit is required. Using this system, the authors measured the pulse arrive time from the electrocardiograph and pulse wave signals in real time, estimated the systolic blood pressure and compared the result with that measured by a cuff sphygmomanometer. The authors found that the root mean square error of the estimated blood pressure and the actual value measured using the cuff sphygmomanometer was 4.5 mmHg or less, and the correlation coefficient was >0.6 with a P value much <0.05. These results show the validity of the developed system for cuffless and continuous blood pressure estimation.

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.

Severity detection tool for patients with infectious disease

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.

Detection and classification of ECG noises using decomposition on mixed codebook for quality analysis

In this Letter, a robust technique is presented to detect and classify different electrocardiogram (ECG) noises including baseline wander (BW), muscle artefact (MA), power line interference (PLI) and additive white Gaussian noise (AWGN) based on signal decomposition on mixed codebooks. These codebooks employ temporal and spectral-bound waveforms which provide sparse representation of ECG signals and can extract ECG local waves as well as ECG noises including BW, PLI, MA and AWGN simultaneously. Further, different statistical approaches and temporal features are applied on decomposed signals for detecting the presence of the above mentioned noises. The accuracy and robustness of the proposed technique are evaluated using a large set of noise-free and noisy ECG signals taken from the Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH polysmnographic database and Fantasia database. It is shown from the results that the proposed technique achieves an average detection accuracy of above 99% in detecting all kinds of ECG noises. Furthermore, average results show that the technique can achieve an average sensitivity of 98.55%, positive productivity of 98.6% and classification accuracy of 97.19% for ECG signals taken from all three databases.

Non-invasive reconstruction of dynamic myocardial transmembrane potential with graph-based total variation constraints

Non-invasive reconstruction of electrophysiological activity in the heart is of great significance for clinical disease prevention and surgical treatment. The distribution of transmembrane potential (TMP) in three-dimensional myocardium can help us diagnose heart diseases such as myocardial ischemia and ectopic pacing. However, the problem of solving TMP is ill-posed, and appropriate constraints need to be added. The existing state-of-art method total variation minimisation only takes advantage of the local similarity in space, which has the problem of over-smoothing, and fails to take into account the relationship among frames in the dynamic TMP sequence. In this work, the authors introduce a novel regularisation method called graph-based total variation to make up for the above shortcomings. The graph structure takes the TMP value of a time sequence on each heart node as the criterion to establish the similarity relationship among the heart. Two sets of phantom experiments were set to verify the superiority of the proposed method over the traditional constraints: infarct scar reconstruction and activation wavefront reconstruction. In addition, experiments with ten real premature ventricular contractions patient data were used to demonstrate the accuracy of the authors’ method in clinical applications.

Stethoscope with digital frequency translation for improved audibility

The performance of an acoustic stethoscope is improved by translating, without loss of fidelity, heart sounds, chest sounds, and intestinal sounds below 50 Hz into a frequency range of 200 Hz, which is easily detectable by the human ear. Such a frequency translation will be of significant benefit to hearing impaired physicians and it will improve the stethoscope performance in a noisy environment. The technique is based on a single sideband suppressed carrier modulation. Stability and bias problems commonly associated with an analog frequency translator are avoided by an all-digital implementation. Real-time audio processing is made possible by approximating a Hilbert transformer with a time delay. The performance of the digital frequency translator was verified with a 16-bit 44.1 Ks/s audio coder/decoder and a 32-bit 72 MHz microcontroller.

Research on a physical activity tracking system based upon three-axis accelerometer for patients with leg ulcers

Venous leg ulcerations are a common problem, with high prevalence in the middle-aged and elderly population, and more attention on research of their physical activities has been paid, as they have great effects on the blood circulation of the lower limb. With enough, appropriate training, the chronic venous ulcerations in the lower limb can be avoided and alleviated, and venous hypertension can be reduced effectively. The study deals with a physical activity tracking system for the patients based on a three-axis accelerometer. The system uses a three-axis accelerometer, a microcontroller, and a wireless Bluetooth module to form a data acquisition platform to acquire accelerations of the lower limb movement, and sends it to a smart mobile phone via the wireless Bluetooth module. The system takes advantages of the smart mobile phone to guide the chronic venous leg ulcers to do prescribed rehabilitation exercises for the lower limb muscles, perform acceleration data preprocessing, wavelet transform and reconstruction, denoising and feature extraction, obtain the results of the rehabilitation exercises, and then give reasonable evaluation and judgment. It is helpful to treat underlying venous reflux, create such an environment that allows skin to grow across an ulcer, and accelerate ulcer healing process consequently.

Selection of optimum frequency bands for detection of epileptiform patterns

The significant research effort in the domain of epilepsy has been directed toward the development of an automated seizure detection system. In their usage of the electrophysiological recordings, most of the proposals thus far have followed the conventional practise of employing all frequency bands following signal decomposition as input features for a classifier. Although seemingly powerful, this approach may prove counterproductive since some frequency bins may not carry relevant information about seizure episodes and may, instead, add noise to the classification process thus degrading performance. A key thesis of the work described here is that the selection of frequency subsets may enhance seizure classification rates. Additionally, the authors explore whether a conservative selection of frequency bins can reduce the amount of training data needed for achieving good classification performance. They have found compelling evidence that using spectral components with <25 Hz frequency in scalp electroencephalograms can yield state-of-the-art classification accuracy while reducing training data requirements to just a tenth of those employed by current approaches.

Influence of mental stress on the pulse wave features of photoplethysmograms

Mental stress is a major burden for our society. Invasive and non-invasive methods have been proposed to monitor and quantify it using various sensors on and off body. In this Letter, the authors investigated the use of the arm photoplethysmogram (PPG) to assess mental stress in laboratory conditions. Results were in correspondence with their previous in-silico study which guided the present study. Three wave shape parameters were identified for stress assessment from the PPG signal: (i) the time from dicrotic notch to end diastole; (ii) the time from pulse onset to systolic peak; and (iii) the ratio of diastolic to systolic area. The proposed in-vivo results showed that the two first parameters responded significantly to increased mental stress and to a breathing relaxation procedure, complementing heart rate, heart rate variability, and pulse transit time as indices of stress.

Diagnosis of attention deficit hyperactivity disorder using non-linear analysis of the EEG signal

Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may be found in 5%-8% of the children. Early diagnosis of ADHD is crucial for treating the disease and reducing its harmful effects on education, employment, relationships, and life quality. On the other hand, non-linear analysis methods are widely applied in processing the electroencephalogram (EEG) signals. It has been proved that the brain neuronal activity and its related EEG signals have chaotic behaviour. Hence, chaotic indices can be employed to classify the EEG signals. In this study, a new approach is proposed based on the combination of some non-linear features to distinguish ADHD from normal children. Lyapunov exponent, fractal dimension, correlation dimension and sample, fuzzy and approximate entropies are the non-linear extracted features. For computing, the chaotic time series of obtained EEG in the brain frontal lobe (FP1, FP2, F3, F4, and Fz) need to be analysed. Experiments on a set of EEG signal obtained from 50 ADHD and 26 normal cases yielded a sensitivity, specificity, and accuracy of 98, 92.31, and 96.05%, respectively. The obtained accuracy provides a significant improvement in comparison to the other similar studies in identifying and classifying children with ADHD.

Internet of things based multi-sensor patient fall detection system

Accidental falls of patients cannot be completely prevented. However, timely fall detection can help prevent further complications such as blood loss and unconsciousness. In this study, the authors present a cost-effective integrated system designed to remotely detect patient falls in hospitals in addition to classifying non-fall motions into activities of daily living. The proposed system is a wearable device that consists of a camera, gyroscope, and accelerometer that is interfaced with a credit card-sized single board microcomputer. The information received from the camera is used in a visual-based classifier and the sensor data is analysed using the k-Nearest Neighbour and Naïve Bayes' classifiers. Once a fall is detected, an attendant at the hospital is informed. Experimental results showed that the accuracy of the device in classifying fall versus non-fall activity is 95%. Other requirements and specifications are discussed in greater detail.

Use of zero-frequency resonator for automatically detecting systolic peaks of photoplethysmogram signal

This work investigates the application of zero-frequency resonator (ZFR) for detecting systolic peaks of photoplethysmogram (PPG) signals. Based on the authors' studies, they propose an automated noise-robust method, which consists of the central difference operation, the ZFR, the mean subtraction and averaging, the peak determination, and the peak rejection/acceptance rule. The method is evaluated using different kinds of PPG signals taken from the standard MIT-BIH polysomnographic database and Complex Systems Laboratory database and the recorded PPG signals at their Biomedical System Lab. The method achieves an average sensitivity (Se) of 99.95%, positive predictivity (Pp) of 99.89%, and overall accuracy (OA) of 99.84% on a total number of 116,673 true peaks. Evaluation results further demonstrate the robustness of the ZFR-based method for noisy PPG signals with a signal-to-noise ratio (SNR) ranging from 30 to 5 dB. The method achieves an average Se = 99.76%, Pp = 99.84%, and OA = 99.60% for noisy PPG signals with a SNR of 5 dB. Various results show that the method yields better detection rates for both noise-free and noisy PPG signals. The method is simple and reliable as compared with the complexity of signal processing techniques and detection performance of the existing detection methods.

Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain

Detection of epileptogenic focus based on electroencephalogram (EEG) signal screening is an important pre-surgical step to remove affected regions inside the human brain. Considering the fact above, in this work, a novel technique for detection of focal EEG signals is proposed using a combination of empirical mode decomposition (EMD) and Teager–Kaiser energy operator (TKEO). EEG signals belonging to focal (Fo) and non-focal (NFo) groups were at first decomposed into a set of intrinsic mode functions (IMFs) using EMD. Next, TKEO was applied on each IMF and two higher-order statistical moments namely skewness and kurtosis were extracted as features from TKEO of each IMF. The statistical significance of the selected features was evaluated using student's t-test and based on the statistical test, features from first three IMFs which show very high discriminative capability were selected as inputs to a support vector machine classifier for discrimination of Fo and NFo signals. It was observed that the classification accuracy of 92.65% is obtained in classifying EEG signals using a radial basis kernel function, which demonstrates the efficacy of proposed EMD-TKEO based feature extraction method for computer-based treatment of patients suffering from focal seizures.

Automatic detection of sleep apnea events based on inter-band energy ratio obtained from multi-band EEG signal

Sleep apnea is a potentially serious sleep disorder characterised by abnormal pauses in breathing. Electroencephalogram (EEG) signal analysis plays an important role for detecting sleep apnea events. In this research work, a method is proposed on the basis of inter-band energy ratio features obtained from multi-band EEG signals for subject-specific classification of sleep apnea and non-apnea events. The K-nearest neighbourhood classifier is used for classification purpose. Unlike conventional methods, instead of classifying apnea patient and healthy person, the objective here is to differentiate apnea and non-apnea events of an apnea patient, which makes the task very challenging. Extensive experimentation is carried out on EEG data of several subjects obtained from a publicly available database. Comprehensive experimental results reveal that the proposed method offers very satisfactory classification performance in terms of sensitivity, specificity and accuracy.

Implementation of the pulse rhythmic rate for the efficient diagnosing of the heartbeat

The mortality rate has risen due to the increase in number of cardiac patients in recent times due to the lack of unawareness of the symptoms. This work mainly aims to detect the anomalies of the rhythmic conditions of the pulse derived from the electrocardiogram (ECG) pattern based on correlation and the method of mapping. As this device is a programmable one and a real-time application wearable system on the wrist which is physically connected to the veins, it continuously monitors the photoplethysmography (PPG) pattern based on certain parameters and rhythmic conditions, it ensures whether the patient is under the safe condition or not. The salient features of PPG waveform are extracted with respect to various abnormal categories of ECG beats subdivided into various time durations of one, two and three. The PPG pattern using various feature extraction and the correlation transforms with the signal processing application. The extracted features help to find the skipped beat with irregularities of the rhythm will activate the emergency condition protocol in the device. The location of the patient with a critical condition is sent to the nearest health centre. This innovation is a portable one and a user-friendly application which can save many lives in the society.

Network analysis through the use of joint-distribution entropy on EEG recordings of MCI patients during a visual short-term memory binding task

The early diagnosis of Alzheimer's disease (AD) is particularly challenging. Mild cognitive impairment (MCI) has been linked to AD and electroencephalogram (EEG) recordings are able to measure brain activity directly with high temporal resolution. In this context, with appropriate processing, the EEG recordings can be used to construct a graph representative of brain functional connectivity. This work studies a functional network created from a non-linear measure of coupling of beta-filtered EEG recordings during a short-term memory binding task. It shows that the values of the small-world characteristic and eccentricity are, respectively, lower and higher in MCI patients than in controls. The results show how MCI leads to EEG functional connectivity changes. They expect that the network differences between MCIs and control subjects could be used to gain insight into the early stages of AD.

Benchmarking of the BITalino biomedical toolkit against an established gold standard

The low-cost multimodal platform BITalino is being increasingly used for educational and research purposes. However, there is still a lack of well-structured work comparing data acquired by this toolkit against a reference device, using established experimental protocols. This work intends to fill the said gap by benchmarking the performance of BITalino against the BioPac MP35 Student Lab Pro device. This work followed a methodical experimental protocol to acquire data from the two devices simultaneously. Four physiological signals were acquired: electrocardiography, electromyography, electrodermal activity and electroencephalography. Root mean square error and coefficient of determination were computed to analyse differences between BITalino and BioPac. Electrodermal activity signals were very similar for the two devices, even without applying any major signal processing techniques. For electrocardiography, a simple morphological comparison also revealed high similarity between devices, and this similarity increased after a common segmentation procedure was followed. Regarding electromyography and electroencephalography data, the approach consisted of comparing features extracted using common post-processing methods. The differences between BITalino and BioPac were again small. Overall, the results presented here show a close similarity between data acquired by the BITalino and by the reference device. This is an important validation step for all researchers working with this multimodal platform.

Estimation of respiratory rate from motion contaminated photoplethysmography signals incorporating accelerometry

Estimation of respiratory rate (RR) from photoplethysmography (PPG) signals has important applications in the healthcare sector, from assisting doctors onwards to monitoring patients in their own homes. The problem is still very challenging, particularly during the motion for large segments of data, where results from different methods often do not agree. The authors aim to propose a new technique which performs motion reduction from PPG signals with the help of simultaneous acceleration signals where the PPG and accelerometer sensors need to be embedded in the same sensor unit. This method also reconstructs motion corrupted PPG signals in the Hilbert domain. An auto-regressive (AR) based technique has been used to estimate the RR from reconstructed PPGs. The proposed method has provided promising results for the estimation of RRs and their variations from PPG signals corrupted with motion artefact. The proposed platform is able to contribute to continuous in-hospital and home-based monitoring of patients using PPG signals under various conditions such as rest and motion states.

Changes in lower limb muscle synchronisation during walking on high-heeled shoes

The goal of this research was to investigate the effect of wearing high-heeled shoes (HHS) on lower limb muscle synchronisation during walking, using beta band (15-30 Hz) coherence analysis. Fifteen females with no previous neuromuscular disorders volunteered in this study. Surface electromyography in frequency domain was studied from rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM) and semitendinosus (ST) muscles during walking by subjects wearing HHS of three different heel heights (low - 4 cm, medium - 6 cm and high - 10 cm). Average coherence values were calculated for RF-VL, RF-VM and RF-ST muscles in beta band to analyse muscle pair synchronisation. In this study, significant increase in beta band coherence was found in all three muscle pairs during walking on HHS of different heel heights (p<0.05). Increased beta band coherence obtained from this study suggested that walking on HHS demands higher muscle pair synchronisation, to maintain stability around the knee joint.

Experience-based SEEG planning: from retrospective data to automated electrode trajectories suggestions

StereoElectroEncephaloGraphy (SEEG) is a minimally invasive technique that consists of the insertion of multiple intracranial electrodes to precisely identify the epileptogenic focus. The planning of electrode trajectories is a cumbersome and time-consuming task. Current approaches to support the planning focus on electrode trajectory optimisation based on geometrical constraints but are not helpful to produce an initial electrode set to begin with the planning procedure. In this work, the authors propose a methodology that analyses retrospective planning data and builds a set of average trajectories, representing the practice of a clinical centre, which can be mapped to a new patient to initialise planning procedure. They collected and analysed the data from 75 anonymised patients, obtaining 30 exploratory patterns and 61 mean trajectories in an average brain space. A preliminary validation on a test set showed that they were able to correctly map 90% of those trajectories and, after optimisation, they have comparable or better values than manual trajectories in terms of distance from vessels and insertion angle. Finally, by detecting and analysing similar plans, they were able to identify eight planning strategies, which represent the main tailored sets of trajectories that neurosurgeons used to deal with the different patient cases.

Understanding perception of active noise control system through multichannel EEG analysis

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p \lt 0.001$\end{document}p<0.001) and 99.31% (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$p \lt 0.001$\end{document}p<0.001), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

Using brain connectivity metrics from synchrostates to perform motor imagery classification in EEG-based BCI systems

Phase synchronisation between different neural groups is considered an important source of information to understand the underlying mechanisms of brain cognition. This Letter investigated phase-synchronisation patterns from electroencephalogram (EEG) signals recorded from ten healthy participants performing motor imagery (MI) tasks using schematic emotional faces as stimuli. These phase-synchronised states, named synchrostates, are specific for each cognitive task performed by the user. The maximum and minimum number of occurrence states were selected for each subject and task to extract the connectivity network measures based on graph theory to feed a set of classification algorithms. Two MI tasks were successfully classified with the highest accuracy of 85% with corresponding sensitivity and specificity of 85%. In this work, not only the performance of different supervised learning techniques was studied, as well as the optimal subset of features to obtain the best discrimination rates. The robustness of this classification method for MI tasks indicates the possibility of expanding its use for online classification of the brain-computer interface (BCI) systems.

Effect of technique and transfer board use on the performance of wheelchair transfers

Transferring to and from the wheelchair seat is a necessary skill for many wheelchair users who wish to be independent of their everyday life. The performance of wheelchair transfers has been associated with the risk of falling and developing upper limb injuries. Both present a risk to the independence of the individual. Previous studies on wheelchair transfers have focused mainly on the analysis of sitting transfers performed by individuals with spinal cord injury, which only represent a small portion of the wider wheelchair users’ population. The purpose of this study is to investigate the effect of different transferring techniques (sitting, standing) and transfer board use on the ground reaction forces under the hands during transfer performance and transfer quality measured using the transfer assessment instrument (TAI). Sitting transfers displayed generally higher peak and mean reaction forces underneath both leading and trailing hands compared with the other techniques, but the difference was only significant between sitting and standing transfers. Standing transfers had significantly lower TAI scores compared with sitting transfer, potentially indicating a decreased level of safety associated with their performance. Transfer boards were only partially effective in reducing the weight born by the upper limbs and they caused only a minor reduction in the overall TAI score in comparison to sitting transfers.

Remote examination of exudates-impact of macular oedema

One of the major causes of eye blindness is identified to be as diabetic retinopathy, which if not detected in earlier stage would cause a serious issue. Long-term diabetes causes diabetic retinopathy. The significant key factor leading to diabetic retinopathy is exudates which affect the retina part and causes eye defects. Thus the first and foremost task in the automated detection of macular oedema is to detect the presence of these exudates. The authors use image processing techniques to detect the optic disc, exudates and the presence of macular oedema. Their method has the sensitivity 96.07%, selectivity 97.36%, and accuracy 96.62% for the exudates detection and in the case of macular oedema detection the sensitivity 97.75%, selectivity 100%, and accuracy 98.86% is achieved. The performance comparison with other methods reveals that their method can be used as a screening process for diabetic retinopathy. In addition to that, the algorithm can help to detect macular oedema.

Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Profiling the propagation of error from PPG to HRV features in a wearable physiological-monitoring device

Wearable physiological monitors are becoming increasingly commonplace in the consumer domain, but in literature there exists no substantive studies of their performance when measuring the physiology of ambulatory patients. In this Letter, the authors investigate the reliability of the heart-rate (HR) sensor in an exemplar ‘wearable’ wrist-worn monitoring system (the Microsoft Band 2); their experiments quantify the propagation of error from (i) the photoplethysmogram (PPG) acquired by pulse oximetry, to (ii) estimation of HR, and (iii) subsequent calculation of HR variability (HRV) features. Their experiments confirm that motion artefacts account for the majority of this error, and show that the unreliable portions of HR data can be removed, using the accelerometer sensor from the wearable device. The experiments further show that acquired signals contain noise with substantial energy in the high-frequency band, and that this contributes to subsequent variability in standard HRV features often used in clinical practice. The authors finally show that the conventional use of long-duration windows of data is not needed to perform accurate estimation of time-domain HRV features.

Cascade and parallel combination (CPC) of adaptive filters for estimating heart rate during intensive physical exercise from photoplethysmographic signal

Photoplethysmographic (PPG) signal is getting popularity for monitoring heart rate in wearable devices because of simplicity of construction and low cost of the sensor. The task becomes very difficult due to the presence of various motion artefacts. In this study, an algorithm based on cascade and parallel combination (CPC) of adaptive filters is proposed in order to reduce the effect of motion artefacts. First, preliminary noise reduction is performed by averaging two channel PPG signals. Next in order to reduce the effect of motion artefacts, a cascaded filter structure consisting of three cascaded adaptive filter blocks is developed where three-channel accelerometer signals are used as references to motion artefacts. To further reduce the affect of noise, a scheme based on convex combination of two such cascaded adaptive noise cancelers is introduced, where two widely used adaptive filters namely recursive least squares and least mean squares filters are employed. Heart rates are estimated from the noise reduced PPG signal in spectral domain. Finally, an efficient heart rate tracking algorithm is designed based on the nature of the heart rate variability. The performance of the proposed CPC method is tested on a widely used public database. It is found that the proposed method offers very low estimation error and a smooth heart rate tracking with simple algorithmic approach.

Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNRS is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.

Speech reconstruction using a deep partially supervised neural network

Statistical speech reconstruction for larynx-related dysphonia has achieved good performance using Gaussian mixture models and, more recently, restricted Boltzmann machine arrays; however, deep neural network (DNN)-based systems have been hampered by the limited amount of training data available from individual voice-loss patients. The authors propose a novel DNN structure that allows a partially supervised training approach on spectral features from smaller data sets, yielding very good results compared with the current state-of-the-art.

Electrocardiograph signal denoising based on sparse decomposition

Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT-BIH arrhythmia database, compared with wavelet transform (WT)-based method. Signal-noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.

Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI

Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors’ experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.

Motion artefact removals for wearable ECG using stationary wavelet transform

Wearable Electrocardiogram (ECG) is attracting much attention in daily healthcare applications. From the viewpoint of long-term use, it is desired that the electrodes are non-contact with the human body. In this study, the authors propose an algorithm using the stationary wavelet transform (SWT) to remove motion artefact superimposed on ECG signal when using non-contact capacitively coupling electrodes. The authors evaluate the effect on motion artefact removal of this algorithm by applying it to various ECG signals with motion artefacts superimposed. As a result, the correlation coefficients of ECG signals with respect to the clean ones have been improved from 0.71 to 0.88 on median before and after motion artefact removal, which demonstrates the validity of the proposed SWT-based algorithm.

Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals

In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.

Common-mode noise cancellation circuit for wearable ECG

Wearable electrocardiogram (ECG) is attracting much attention for monitoring heart diseases in healthcare and medical applications. However, an imbalance usually exists between the contact resistances of sensing electrodes, so that a common mode noise caused by external electromagnetic field can be converted into the ECG detection circuit as a differential mode interference voltage. In this study, after explaining the mechanism of how the common mode noise is converted to a differential mode interference voltage, the authors propose a circuit with cadmium sulphide photo-resistors for cancelling the imbalance between the contact resistances and confirm its validity by simulation experiment. As a result, the authors found that the interference voltage generated at the wearable ECG can be effectively reduced to a sufficient small level.

ECG signal analysis using modified S-transform

Accurate detection of QRS complexes is essential for the investigation of heart rate variability. Several transform techniques have been proposed and extensively used for the detection and analysis of QRS complexes. In this proposed work, the de-noised ECG signal is subjected to a modified S-transform for QRS complex detection.The performance analysis of the proposed work is evaluated using parameters such as sensitivity, positive predictivity and accuracy. The algorithm delivers sensitivity, positive predictivity and overall accuracy of 99.91, 99.91 and 99.77%, respectively. Furthermore, a search back mechanism is employed, which specifies the filtered electrocardiogram (ECG) segment, which was traced for the true R-peak locations. The modified S-transform based QRS complex detection algorithm provides an excellent search back range of only ±2 samples in comparison with other earlier proposed algorithms.

Feasibility of detecting orthopaedic screw overtightening using acoustic emission

A preliminary study of acoustic emission during orthopaedic screw fixation was performed using polyurethane foam as the bone-simulating material. Three sets of screws, a dynamic hip screw, a small fragment screw and a large fragment screw, were investigated, monitoring acoustic-emission activity during the screw tightening. In some specimens, screws were deliberately overtightened in order to investigate the feasibility of detecting the stripping torque in advance. One set of data was supported by load cell measurements to directly measure the axial load through the screw. Data showed that acoustic emission can give good indications of impending screw stripping; such indications are not available to the surgeon at the current state of the art using traditional torque measuring devices, and current practice relies on the surgeon's experience alone. The results suggest that acoustic emission may have the potential to prevent screw overtightening and bone tissue damage, eliminating one of the commonest sources of human error in such scenarios.

Enhanced inter-subject brain computer interface with associative sensorimotor oscillations

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

Analysis of physiological signals using state space correlation entropy

In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.

Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal

Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Continuous patient monitoring systems acquire enormous amounts of data that is either manually analysed by doctors or automatically processed using intelligent algorithms. Sections of data acquired over long period of time can be corrupted with artefacts due to patient movement, sensor placement and interference from other sources. Owing to the large volume of data these artefacts need to be automatically identified so that the analysis systems and doctors are aware of them while making medical diagnosis. Three important factors are explored that must be considered and quantified for the design and evaluation of automatic artefact identification algorithms: signal quality, interpretation quality and computational complexity. The first two are useful to determine the effectiveness of an algorithm, whereas the third is particularly vital in mHealth systems where computational resources are heavily constrained. A series of artefact identification and filtering algorithms are then presented focusing on the electrocardiography data. These algorithms are quantified using the three metrics to demonstrate how different algorithms can be evaluated and compared to select the best ones for a given wireless sensor network.

Characterisation of the complexity of intracranial pressure signals measured from idiopathic and secondary normal pressure hydrocephalus patients

Hydrocephalus is a condition characterised by enlarged cerebral ventricles, which in turn affects intracranial pressure (ICP); however, the mechanisms regulating ICP are not fully understood. A nonlinear signal processing approach was applied to ICP signals measured during infusion studies from patients with two forms of hydrocephalus, in a bid to compare the differences. This is the first study of its kind. The two forms of hydrocephalus were idiopathic normal pressure hydrocephalus (iNPH) and secondary normal pressure hydrocephalus (SH). Following infusion tests, the Lempel-Ziv (LZ) complexity was calculated from the iNPH and SH ICP signals. The LZ complexity values were averaged for the baseline, infusion, plateau and recovery stages of the tests. It was found that as the ICP increased from basal levels, the LZ complexities decreased, reaching their lowest during the plateau stage. However, the complexities computed from the SH ICP signals decreased to a lesser extent when compared with the iNPH ICP signals. Furthermore, statistically significant differences were found between the plateau and recovery stage complexities when comparing the iNPH and SH results (p = 0.05). This Letter suggests that advanced signal processing of ICP signals with LZ complexity can help characterise different types of hydrocephalus in more detail.