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Martin Golz

HS-Schmalkalden, Computer Science, Faculty Member

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Phone: +49 3683 688 4107
Address: University of Applied Sciences Schmalkalden
Faculty of Computer Science
Blechhammer 4
98574 Schmalkalden
Germany

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Papers by Martin Golz

All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more

Wachheitsmonitoring im Fahrzeug - technische Loesungen

Die Einschaetzung von Ueberwachungsgeraeten ist von Bedeutung, um Schlaefrigkeit unter Aufmerksam... more Die Einschaetzung von Ueberwachungsgeraeten ist von Bedeutung, um Schlaefrigkeit unter Aufmerksamkeitsanforderungen zuverlaessig messen und damit bewerten zu koennen. In einem von Caterpillar Machine Research, Peoria (USA) beauftragten Projekt wurden drei Messgeraete ausgewaehlt und ihre Leistungsfaehigkeit im Fahrsimulationslabor der Fachhochschule Schmalkalden untersucht. Alle Systeme nutzen die PERCLOS-Variable, die sich aus dem mittleren relativen Anteil der Lidschlussdauer bezogen auf ein Zeitintervall ergibt. Die Praesentation stellt die Ergebnisse vor. Beitrag zum Workshop 5 des 6. Gemeinsamen Symposiums der DGVM und DGVP "Tagesschlaefrigkeit - Gefahren und Konsequenzen fuer den Strassenverkehr" am 1. - 2. Oktober 2010 in Tuebingen. Siehe auch Gesamtaufnahme des Symposiums, ITRD D368425.

EEG-Based Classification of the Driver Alertness State

Current Directions in Biomedical Engineering, 2020

GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning... more GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.

Inter-individual variability of EEG features during microsleep events

Current Directions in Biomedical Engineering, 2019

This paper examines the question of how strongly the spectral properties of the EEG during micros... more This paper examines the question of how strongly the spectral properties of the EEG during microsleep differ between individuals. For this purpose, 3859 microsleep examples were compared with 4044 counterexamples in which drivers were very drowsy but were able to perform the driving task. Two types of signal features were compared: logarithmic power spectral densities and entropy measures of wavelets coefficient series. Discriminant analyses were performed with the following machine learning methods: support-vector machines, gradient boosting, learning vector quantization. To the best of our knowledge, this is the first time that results of the leave-one-subject-out cross-validation (LOSO CV) for the detection of microsleep are presented. Error rates lower than 5.0 % resulted in 17 subjects and lower than 13 % in another 11 subjects. In 3 individuals, EEG features could not be explained by the pool of EEG features of all other individuals; for them, detection errors were 15.1 %, 17....

Phonetisch-akustische Schläfrigkeitsdetektion

Somnologie - Schlafforschung und Schlafmedizin, 2010

Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals

Current Directions in Biomedical Engineering, 2017

We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers ... more We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes

Circadian rhythmicity of cognitive performance

Current Directions in Biomedical Engineering, 2017

It was investigated whether cognitive performance shows a circadian rhythm during a 50 h-long for... more It was investigated whether cognitive performance shows a circadian rhythm during a 50 h-long forced desynchrony sleep-wake-schedule. We asked whether it would be possible to estimate the circadian period of cognitive performance under such circumstances and how strong it correlates to subjective sleepiness rating as well as body temperature. Cognitive performance was evaluated using the Bowles-Langley test (BLT), which estimates cognitive performance by capturing reaction time and counting errors in a 2-choice visual search task. Power spectral densities (PSD) were estimated by the Lomb-Scargle periodogram [1]. The circadian period τc was estimated from peak PSD. PSD of BLT scores showed lowest, yet distinct, circadian amplitude. In addition to the circadian period estimation we analyzed the correlation of the acquired variables against each other. Pearson’s correlation coefficients were significant but varied strongly at a commonly low level. Despite the obstacle of a plethora of ...

EOG feature relevance determination for microsleep detection

Current Directions in Biomedical Engineering, 2017

Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep ... more Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean clas...

Detection of microsleep events in a car driving simulation study using electrocardiographic features

Current Directions in Biomedical Engineering, 2016

Microsleep events (MSE) are short intrusions of sleep under the demand of sustained attention. Th... more Microsleep events (MSE) are short intrusions of sleep under the demand of sustained attention. They can impose a major threat to safety while driving a car and are considered one of the most significant causes of traffic accidents. Driver’s fatigue and MSE account for up to 20% of all car crashes in Europe and at least 100,000 accidents in the US every year. Unfortunately, there is not a standardized test developed to quantify the degree of vigilance of a driver. To account for this problem, different approaches based on biosignal analysis have been studied in the past. In this paper, we investigate an electrocardiographic-based detection of MSE using morphological and rhythmical features. 14 records from a car driving simulation study with a high incidence of MSE were analyzed and the behavior of the ECG features before and after an MSE in relation to reference baseline values (without drowsiness) were investigated. The results show that MSE cannot be detected (or predicted) using ...

Prediction of immediately occurring microsleep events from brain electric signals

Current Directions in Biomedical Engineering, 2016

This contribution addresses the question if imminent changes of the cortical state are predictabl... more This contribution addresses the question if imminent changes of the cortical state are predictable. The analysis is based on 1484 examples of microsleep (MS) and 1940 counterexamples of sustained attention (SA), both observed during overnight driving in the simulator. EEG segments (8 s in length) immediately before each respective event were included. Features were extracted by (i) modified periodogram and (ii) Choi-Williams distribution. Machine learning algorithms, namely the optimized learning vector quantization (OLVQ) and the support-vector machine with Gaussian kernel function (SVM), were trained in order to map signal features to the event type (MS or SA). Cross validation analysis yielded test set classification accuracies of 87.5 ± 0.1 % and 82.7 ± 0.1 % for feature set (i) and (ii), respectively. In general, SVM outperformed OLVQ. In conclusion, EEG contains enough information to predict immediately upcoming microsleep events.

Phonetisch-akustische Schl�frigkeitsdetektion

Somnologie Schlafforschung Und Schlafmedizin, 2011

The role of expert evaluation for microsleep detection

Current Directions in Biomedical Engineering, 2015

Recently, it has been shown by overnight driving simulation studies that microsleep density is th... more Recently, it has been shown by overnight driving simulation studies that microsleep density is the only known sleepiness indicator which rapidly increases within a few seconds immediately before sleepiness related crashes. This indicator is based solely on EEG and EOG and subsequent adaptive pattern recognition. Accurate microsleep recognition is very important for the performance of this sleepiness indicator. The question is whether expensive evaluations of microsleep events by a) experts are necessary or b) non-experts provide sufficient evaluations. Based on 11,114 microsleep events in case a) and 12,787 in case b) recognition accuracies were investigated utilizing (i) artificial neural networks and (ii) support-vector machines. Cross validated classification accuracies ranged between 92.2 % for (i,b) and 99.3 % for (ii, a). It is concluded that expert evaluations are very important to provide independent information for detecting microsleep.

V46 Validierung eines PKW-Fahrsimulators für arbeitsmedizinische Fragestellungen: Pilotstudie mit Ethanol

Continuous Wavelet vs. Fourier Transform-based Feature Extraction for Recognizing Sleepiness from Steering Behavior

Balanced Leave-One-Subject-Out Cross- Validation for Microsleep Classification

Current Directions in Biomedical Engineering, 2021

Inter-individual differences in the feature distribution of electroencephalograms (EEG) during mi... more Inter-individual differences in the feature distribution of electroencephalograms (EEG) during microsleep (MS) raise questions about the generalizability of the methodology, because methodology should have the same validity for subjects to be included in the analysis in the future as for subjects included so far. We address this question using leave-one-subject- out cross-validation (LOSO CV) to simulate inclusion of test data from future subjects. Investigations are based on EEG of 70 subjects across four studies conducted in our driving simulation lab. 9,297 MS and 10,264 counter-examples of sustained attention (SA) were included in the analysis. Three variants of sample balancing are compared: (1) none, (2) overall balancing, and (3) between-subjects balancing. Resulting classification accuracies range from 80% to 100% for 67 subjects, but for 3 subjects they are at 66.5%, 73.1%, 78.3%. Averaged across subjects, accuracies were (1) 90.2%, (2) 90.2% and (3) 89.1%. Thus, balancing ...

A Novel Tool for Sequential Fusion of Nonlinear Features: A Sleep Psychology Application

by Martin Golz and Dragan Obradovic

2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2006

A framework for automated scoring of sleep stages during afternoon naps of healthy humans is intr... more A framework for automated scoring of sleep stages during afternoon naps of healthy humans is introduced. This is achieved by sequential fusion of nonlinear features extracted from three physiological channels: the electroencephalogram (EEG), electrooculogram (EOG) and respiratory trace (RES). These features are generated by means of the recently introduced "Delay Vector Variance" (DVV) method which examines local predictability of a signal in phase space. The analysis is accompanied by a set of comprehensive simulations, supporting the approach.

Evaluierung von Ger�ten zur M�digkeits�berwachung

Somnologie Schlafforschung Und Schlafmedizin, 2010

PERCLOS: An Alertness Measure of the Past

1 Circadian Technologies, Inc., Stoneham, Massachusetts, USA 2 Faculty of Computer Science, Unive... more

Asymmetric Properties of Heart Rate Variability to Assess Operator Fatigue

The aim of this study is to evaluate the suitability of heart rate recordings for establishing a ... more The aim of this study is to evaluate the suitability of heart rate recordings for establishing a reliable connection to well-defined fatigue and performance measures in order to estimate fatigue in industrial and transportation applications. An overnight driving simulation scenario with partial sleep deprivation was utilized to induce strong fatigue. An experiment trial was divided into repeated sessions, each of which consisted of a driving performance and two vigilance tasks. Heart rate (HR) was recorded over the entire experiment; HRmeasures were derived and correlated against measures that were established from driving and vigilance task performance and that represent various aspects of operator fatigue. In a previous report ) we presented on the basis of the data of one volunteer that multiple fatigue measures correlate well with different expressions of heart rate variability (HRV), especially with longterm HRV derived from Poincaré plots. In this work, we intensify the Poincaré analysis by dividing the distribution of HR data in different accelerating and decelerating segments and by establishing properties of asymmetry between these segments. We also show that most of the properties of long-term HRV correlate well with specific fatigue measures for a group of 5 volunteers despite their large inter-individual differences in HR-to-fatigue correlations.

Feature Reduction of the EEG in the Spectral Domain

Quantitative EEG analysis in combination with modern Soft Computing methods offers the feasibilit... more Quantitative EEG analysis in combination with modern Soft Computing methods offers the feasibility of processing a large amount of different features. This paper compares empirically the case of no feature reduction to four variants of feature reduction. Investigations were performed on a data set of more than 3,700 examples of microsleep events experienced by young adults in an overnight driving simulation study. More than 4,600 features were extracted from seven EEG and two EOG channels utilizing only the common modified periodogram method to estimate power spectral densities. Investigations revealed band averaging in narrow or in freely optimized spectral bands performed better than no reduction or reduction in common bands of EEG analysis.

All in-text references underlined in blue are linked to publications on ResearchGate, letting you... more

Wachheitsmonitoring im Fahrzeug - technische Loesungen

Die Einschaetzung von Ueberwachungsgeraeten ist von Bedeutung, um Schlaefrigkeit unter Aufmerksam... more Die Einschaetzung von Ueberwachungsgeraeten ist von Bedeutung, um Schlaefrigkeit unter Aufmerksamkeitsanforderungen zuverlaessig messen und damit bewerten zu koennen. In einem von Caterpillar Machine Research, Peoria (USA) beauftragten Projekt wurden drei Messgeraete ausgewaehlt und ihre Leistungsfaehigkeit im Fahrsimulationslabor der Fachhochschule Schmalkalden untersucht. Alle Systeme nutzen die PERCLOS-Variable, die sich aus dem mittleren relativen Anteil der Lidschlussdauer bezogen auf ein Zeitintervall ergibt. Die Praesentation stellt die Ergebnisse vor. Beitrag zum Workshop 5 des 6. Gemeinsamen Symposiums der DGVM und DGVP "Tagesschlaefrigkeit - Gefahren und Konsequenzen fuer den Strassenverkehr" am 1. - 2. Oktober 2010 in Tuebingen. Siehe auch Gesamtaufnahme des Symposiums, ITRD D368425.

EEG-Based Classification of the Driver Alertness State

Current Directions in Biomedical Engineering, 2020

GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning... more GMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.

Inter-individual variability of EEG features during microsleep events

Current Directions in Biomedical Engineering, 2019

This paper examines the question of how strongly the spectral properties of the EEG during micros... more This paper examines the question of how strongly the spectral properties of the EEG during microsleep differ between individuals. For this purpose, 3859 microsleep examples were compared with 4044 counterexamples in which drivers were very drowsy but were able to perform the driving task. Two types of signal features were compared: logarithmic power spectral densities and entropy measures of wavelets coefficient series. Discriminant analyses were performed with the following machine learning methods: support-vector machines, gradient boosting, learning vector quantization. To the best of our knowledge, this is the first time that results of the leave-one-subject-out cross-validation (LOSO CV) for the detection of microsleep are presented. Error rates lower than 5.0 % resulted in 17 subjects and lower than 13 % in another 11 subjects. In 3 individuals, EEG features could not be explained by the pool of EEG features of all other individuals; for them, detection errors were 15.1 %, 17....

Phonetisch-akustische Schläfrigkeitsdetektion

Somnologie - Schlafforschung und Schlafmedizin, 2010

Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals

Current Directions in Biomedical Engineering, 2017

We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers ... more We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes

Circadian rhythmicity of cognitive performance

Current Directions in Biomedical Engineering, 2017

It was investigated whether cognitive performance shows a circadian rhythm during a 50 h-long for... more It was investigated whether cognitive performance shows a circadian rhythm during a 50 h-long forced desynchrony sleep-wake-schedule. We asked whether it would be possible to estimate the circadian period of cognitive performance under such circumstances and how strong it correlates to subjective sleepiness rating as well as body temperature. Cognitive performance was evaluated using the Bowles-Langley test (BLT), which estimates cognitive performance by capturing reaction time and counting errors in a 2-choice visual search task. Power spectral densities (PSD) were estimated by the Lomb-Scargle periodogram [1]. The circadian period τc was estimated from peak PSD. PSD of BLT scores showed lowest, yet distinct, circadian amplitude. In addition to the circadian period estimation we analyzed the correlation of the acquired variables against each other. Pearson’s correlation coefficients were significant but varied strongly at a commonly low level. Despite the obstacle of a plethora of ...

EOG feature relevance determination for microsleep detection

Current Directions in Biomedical Engineering, 2017

Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep ... more Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean clas...

Detection of microsleep events in a car driving simulation study using electrocardiographic features

Current Directions in Biomedical Engineering, 2016

Microsleep events (MSE) are short intrusions of sleep under the demand of sustained attention. Th... more Microsleep events (MSE) are short intrusions of sleep under the demand of sustained attention. They can impose a major threat to safety while driving a car and are considered one of the most significant causes of traffic accidents. Driver’s fatigue and MSE account for up to 20% of all car crashes in Europe and at least 100,000 accidents in the US every year. Unfortunately, there is not a standardized test developed to quantify the degree of vigilance of a driver. To account for this problem, different approaches based on biosignal analysis have been studied in the past. In this paper, we investigate an electrocardiographic-based detection of MSE using morphological and rhythmical features. 14 records from a car driving simulation study with a high incidence of MSE were analyzed and the behavior of the ECG features before and after an MSE in relation to reference baseline values (without drowsiness) were investigated. The results show that MSE cannot be detected (or predicted) using ...

Prediction of immediately occurring microsleep events from brain electric signals

Current Directions in Biomedical Engineering, 2016

This contribution addresses the question if imminent changes of the cortical state are predictabl... more This contribution addresses the question if imminent changes of the cortical state are predictable. The analysis is based on 1484 examples of microsleep (MS) and 1940 counterexamples of sustained attention (SA), both observed during overnight driving in the simulator. EEG segments (8 s in length) immediately before each respective event were included. Features were extracted by (i) modified periodogram and (ii) Choi-Williams distribution. Machine learning algorithms, namely the optimized learning vector quantization (OLVQ) and the support-vector machine with Gaussian kernel function (SVM), were trained in order to map signal features to the event type (MS or SA). Cross validation analysis yielded test set classification accuracies of 87.5 ± 0.1 % and 82.7 ± 0.1 % for feature set (i) and (ii), respectively. In general, SVM outperformed OLVQ. In conclusion, EEG contains enough information to predict immediately upcoming microsleep events.

Phonetisch-akustische Schl�frigkeitsdetektion

Somnologie Schlafforschung Und Schlafmedizin, 2011

The role of expert evaluation for microsleep detection

Current Directions in Biomedical Engineering, 2015

Recently, it has been shown by overnight driving simulation studies that microsleep density is th... more Recently, it has been shown by overnight driving simulation studies that microsleep density is the only known sleepiness indicator which rapidly increases within a few seconds immediately before sleepiness related crashes. This indicator is based solely on EEG and EOG and subsequent adaptive pattern recognition. Accurate microsleep recognition is very important for the performance of this sleepiness indicator. The question is whether expensive evaluations of microsleep events by a) experts are necessary or b) non-experts provide sufficient evaluations. Based on 11,114 microsleep events in case a) and 12,787 in case b) recognition accuracies were investigated utilizing (i) artificial neural networks and (ii) support-vector machines. Cross validated classification accuracies ranged between 92.2 % for (i,b) and 99.3 % for (ii, a). It is concluded that expert evaluations are very important to provide independent information for detecting microsleep.

V46 Validierung eines PKW-Fahrsimulators für arbeitsmedizinische Fragestellungen: Pilotstudie mit Ethanol

Continuous Wavelet vs. Fourier Transform-based Feature Extraction for Recognizing Sleepiness from Steering Behavior

Balanced Leave-One-Subject-Out Cross- Validation for Microsleep Classification

Current Directions in Biomedical Engineering, 2021

Inter-individual differences in the feature distribution of electroencephalograms (EEG) during mi... more Inter-individual differences in the feature distribution of electroencephalograms (EEG) during microsleep (MS) raise questions about the generalizability of the methodology, because methodology should have the same validity for subjects to be included in the analysis in the future as for subjects included so far. We address this question using leave-one-subject- out cross-validation (LOSO CV) to simulate inclusion of test data from future subjects. Investigations are based on EEG of 70 subjects across four studies conducted in our driving simulation lab. 9,297 MS and 10,264 counter-examples of sustained attention (SA) were included in the analysis. Three variants of sample balancing are compared: (1) none, (2) overall balancing, and (3) between-subjects balancing. Resulting classification accuracies range from 80% to 100% for 67 subjects, but for 3 subjects they are at 66.5%, 73.1%, 78.3%. Averaged across subjects, accuracies were (1) 90.2%, (2) 90.2% and (3) 89.1%. Thus, balancing ...

A Novel Tool for Sequential Fusion of Nonlinear Features: A Sleep Psychology Application

by Martin Golz and Dragan Obradovic

2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2006

A framework for automated scoring of sleep stages during afternoon naps of healthy humans is intr... more A framework for automated scoring of sleep stages during afternoon naps of healthy humans is introduced. This is achieved by sequential fusion of nonlinear features extracted from three physiological channels: the electroencephalogram (EEG), electrooculogram (EOG) and respiratory trace (RES). These features are generated by means of the recently introduced "Delay Vector Variance" (DVV) method which examines local predictability of a signal in phase space. The analysis is accompanied by a set of comprehensive simulations, supporting the approach.

Evaluierung von Ger�ten zur M�digkeits�berwachung

Somnologie Schlafforschung Und Schlafmedizin, 2010

PERCLOS: An Alertness Measure of the Past

1 Circadian Technologies, Inc., Stoneham, Massachusetts, USA 2 Faculty of Computer Science, Unive... more

Asymmetric Properties of Heart Rate Variability to Assess Operator Fatigue

The aim of this study is to evaluate the suitability of heart rate recordings for establishing a ... more The aim of this study is to evaluate the suitability of heart rate recordings for establishing a reliable connection to well-defined fatigue and performance measures in order to estimate fatigue in industrial and transportation applications. An overnight driving simulation scenario with partial sleep deprivation was utilized to induce strong fatigue. An experiment trial was divided into repeated sessions, each of which consisted of a driving performance and two vigilance tasks. Heart rate (HR) was recorded over the entire experiment; HRmeasures were derived and correlated against measures that were established from driving and vigilance task performance and that represent various aspects of operator fatigue. In a previous report ) we presented on the basis of the data of one volunteer that multiple fatigue measures correlate well with different expressions of heart rate variability (HRV), especially with longterm HRV derived from Poincaré plots. In this work, we intensify the Poincaré analysis by dividing the distribution of HR data in different accelerating and decelerating segments and by establishing properties of asymmetry between these segments. We also show that most of the properties of long-term HRV correlate well with specific fatigue measures for a group of 5 volunteers despite their large inter-individual differences in HR-to-fatigue correlations.

Feature Reduction of the EEG in the Spectral Domain

Quantitative EEG analysis in combination with modern Soft Computing methods offers the feasibilit... more Quantitative EEG analysis in combination with modern Soft Computing methods offers the feasibility of processing a large amount of different features. This paper compares empirically the case of no feature reduction to four variants of feature reduction. Investigations were performed on a data set of more than 3,700 examples of microsleep events experienced by young adults in an overnight driving simulation study. More than 4,600 features were extracted from seven EEG and two EOG channels utilizing only the common modified periodogram method to estimate power spectral densities. Investigations revealed band averaging in narrow or in freely optimized spectral bands performed better than no reduction or reduction in common bands of EEG analysis.