Can Sequence Mining Improve Your Morning Mood? Toward a Precise Non-invasive Smart Clock Zakaria M. Djedou Fabrice Muhlenbach Pierre Maret Université de Lyon Laboratoire Hubert Curien, UMR CNRS 5516 Université Jean Monnet de Saint-Étienne 18 rue du Professeur Benoît Lauras 42000 SAINT-ÉTIENNE, FRANCE
Guillaume Lopez Graduate School of Science and Engineering Aoyama Gakuin University 5-10-1 Fuchinobe Chuo-ku, Kanagawa, 252-5258 JAPAN
[email protected]
[email protected] [email protected] [email protected] ABSTRACT The aim of this paper is to present our preliminary approach and work in progress in the design of sequence mining techniques for a new smart clock alarm. This clock alarm will ring the user at the most physiological opportune moment in a predefined time frame. We rely on a wearable biosensor collecting various signals (ECG, movement, temperature) and on algorithms that dynamically mine into the sequences of heterogeneous data to identify sleep cycles. The system will be less intrusive and more accurate than others. This paper presents the underlying domains, the method and the experiments we are implementing.
Categories and Subject Descriptors I.2.9 [Robotics]: Sensors; H.2.8 [Database Applications]: Data mining; J.3 [Life and Medical Sciences]: Biology and genetics—Health
General Terms Algorithms, Experimentation, Human Factors, Measurement
Keywords Wearable Sensor, Data Mining, Sequence Mining, Sleep Cycles, Natural Alarm Clock
1.
INTRODUCTION
Thanks to recent sensors it is now possible to monitor a broad spectrum of daily activities, such as physical activity and sleep. Sleep takes up nearly a third of our day, but remains rather mysterious: sleep underlying mechanisms, inPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from
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teractions, and long-term effects are still poorly understood even if great progress has been done in the last years. The quantity and quality of sleep are affected by many factors such as the environment, the society, the culture and internal parameters which can have psychological or physiological origins. Modern society requires longer hours of work curtailing the duration of sleep to fewer hours per day, but many studies suggest an association between too short (and too long) duration of usual sleep (7 to 8 hours) with adverse health outcomes [6]. Progress realized in the field of sensor design tends to fulfil the increasing needs of healthcare services for monitoring patients in a non-invasive way, anywhere and anytime, in order to facilitate the development of wearable, mobile and remote health monitoring systems [1, 2]. In the case of sleep, the need for a non-invasive sensor is even more important: an unsuitable measuring device would disrupt the sleeper. In our study, we are focusing on the waking up by use of an alarm clock. It is desired that the sleeper is in an appropriate sleep stage when the alarm –produced by the device we propose– will ring. We are designing a waking up platform with a set of biometric sensors combined with a processing model provided by data mining techniques. There are currently numerous Android or iPhone smartphone applications supposed to provide this service. They are based on the recording of sound or movement to detect the phases of sleep. The accuracy of these applications is limited and they are inefficient when the sleeper is not alone in the bed. In this position paper, we present on going work for a more personalized system that mixes techniques of data mining and a wearable multi-signal sensor. We aim to provide a comfortable waking up by studying in details the sleep cycles of the sleeper.
2.
STATE OF THE ART
2.1 2.1.1
Sleep Characteristics Physiological Properties of Sleep
The regulation of sleep-wake cycle is controlled by a dual process: circadian and homeostatic. On one hand the circa-
dian process –which is an internal biological clock– is aligned with the alternation of day and night. On the other hand the homeostatic process –which is the tendency to return to a equilibrium state) is a kind of timer that alternates periods of wakefulness and sleep. Moreover, for the human being, sleep timing can be controlled voluntarily (e.g., by using an alarm clock).
2.1.2
Cyclic Variations during the Sleep
From the middle of the 1930s, studies in neurophysiology have taken an interest to the sleep [4, 7]. Some intriguing properties of the electroencephalogram (EEG) have been discovered, especially the close correlation of the EEG characteristic patterns with certain functional states of the organism [8]. From the 1960s, the perspective we had on the sleep and its mechanisms has drastically changed: sleep is an active phenomenon, which contradicts the long held belief that sleep is a passive state of the waking system. With micro-electrode recordings and stimulation, Jouvet [13, 14] has discovered two different states alternating periodically during behavioural sleep: slow-wave sleep and paradoxical sleep which will subsequently be referred to as “REM sleep”. Jouvet was surprised to observe during sleep fast cortical activity –the same or even greater than that of awakening– accompanied by a phenomenon of rapid eye movements (REM) and a total muscular weakness. After this fundamental distinction between REM and nonREM (NREM) states –which play complementary roles now better understood [23]–, later studies will distinguish different phases in the non-REM sleep based on the brain activity recording (EEG) and following an epoch-by-epoch approach to scoring (using epochs of 20 or 30 seconds). These studies provide the following general sleep categorization by retaining the division into wakefulness, NREM, and REM sleep, with 3 stages of NREM sleep [21]: • Stage W (Wakefulness): The person is not sleeping, he or she is conscious and is engaged in coherent cognitive and behavior responses to the external world. For the EEG, > 50% of the page (epoch) consists of alpha (8-13 Hz) activity or low voltage, mixed (2-7 Hz) frequency activity. Alpha waves are recorded during wakeful relaxation with closed eyes but are reduced with open eyes. • Stage N1 (NREM 2 sleep): This is a stage between sleep and wakefulness. The muscles are active, and the eyes roll slowly, opening and closing moderately. 50% of the epoch consists of relatively low voltage mixed (27 Hz) activity, and 75µV ), low frequency (=1 deliberate
NREM Stage high low =1 atonia
• Stage R (REM sleep): The sleeper now enters rapid eye movement (REM) where most muscles are paralyzed. REM sleep is turned on by acetylcholine secretion and is inhibited by neurons that secrete serotonin. This level is also referred to as paradoxical sleep because the sleeper, although exhibiting EEG waves similar to a waking state, is harder to arouse than at any other sleep stage. Vital signs indicate arousal and oxygen consumption by the brain is higher than when the sleeper is awake. Relatively low voltage mixed (2-7 Hz) frequency EEG with episodic rapid eye movements and absent or reduced chin EMG activity. Sleep proceeds in cycles of REM and NREM, usually four or five of them per night, the order normally being N1 → N2 → N3 → N2 → REM. There is a greater amount of deep sleep (stage N3) earlier in the night, while the proportion of REM sleep increases in the two cycles just before natural awakening. An adult reaches REM approximately every 90 minutes, with the latter half of sleep being more dominated by this stage. REM sleep occurs as a person returns to stage 1 from a deep sleep. Other changes were observed during sleep, as presented on Table 1. Some of these changes can be much less identified by the EEG as they concern the autonomic nervous system (ANS) which acts as an unconscious control system (heart rate, digestion, respiratory rate, salivation, etc.) In the ANS, a distinction is classically made between the sympathetic nervous system (SNS) –which acts for mobilizing the body’s nervous system fight-or-flight response and for maintaining homeostasis– and the parasympathetic nervous system (PSNS) –which acts for mobilizing the body’s nervous system rest-and-digest activities and which is responsible for regulation of internal organs and glands.
2.1.3
Sleep Characteristic Measurements
The electroencephalography (EEG) is the recording technique of electrical activity along the scalp. This technique is expected to detect the different sleep stages. The electrooculography (EOG) is a technique for measuring the corneo-retinal standing potential that exists between the front and the back of the human eye. This helps to determine REM sleep, characterized by rapid eye movements. The electromyography (EMG) is used to measure muscle tension in the body as well as to monitor leg movements. It helps to determine when sleep occurs as well as REM sleep. The electrocardiography (ECG) The electrodes measure the electrical activity of the heart as it contracts and expands. It has been shown that the heart rate tends to slow down during the deep sleep phases. A slightly more rapid heart beat is a sign for REM sleep.
2.1.4
Waking Up and Sleep Cycles
The sleep cycle plays a very important role regarding the time of waking up. Some studies suggest that the sleepers prefer to be waked up during REM sleep compared to
NREM. If a sleeper is awaken during a slow-wave sleep or other NREM cycles, the body is not ready to be awake, which leads to an unpleasant sensation during wakefulness: irritability, tiredness feeling, memory problems, or even depression... In this case, the sleeper wakes up cranky and begin the day by considering it as a bad day. If the sleep is not interrupted artificially (by the alarm clock), deep sleep cycles are reducing during the night and the phases of REM sleep increase. The sleeper naturally wakes up during REM sleep.
2.1.5
• modeling and learning methods, which consists of building a model able to retrieve knowledge from the data. Detailed description and a comparison between the different algorithms of data mining can be found in [3]. Furthermore, the authors talk about one of the biggest challenges in the field which consists of the exploitation of multiple measurements of the vital signs simultaneously. Therefore, our aim is clearly to look for a new technique to exploit such combined measurements with the objective of improving results obtained from single signal analysis.
Natural Waking Up Devices
There are already several systems that attempt to wake up a person in a “natural way” or during the REM sleep: • The progressive light, which is not a clock alarm providing a sound but a lamp that lights up progressively to mimic a sun rising; nevertheless this technique does not take into account the personal metabolism of the sleeper. • Smartphone application (e.g., on iPhone) monitors the sleeper movements using the accelerometer of the phone placed in the bed or nearby. This application does not work properly in different cases such as people who do a lot of movement during their sleep, people sharing the bed, unexpected movements (cat approaching), etc.. This application only relies on movements. • Systems monitoring the brain activity. This method requires applying generally sensitive electrodes on the hair, and some cases requires a complete shave for better reading the brain activity. • The last method consists in the detection of eye movement. During the REM stage, the sleeper eyes make many rapid back and forth movements. By putting an infrared signal on the eye and measuring the reflection, oscillations in the signal will indicate that the sleeper is in the REM sleep.
3.
TOWARDS THE MINING OF HETEROGENEOUS DATA SEQUENCES FOR REM STAGE IDENTIFICATION
3.1
Data Recording and Wearable Sensor
Sensors can be classified into 3 categories [1]: • urban sensors, windows and doors sensors, • personal sensors like smartphones or GPS, • wearable sensor. The sensor we are using for our studies belong to the last category. Actually this sensor, called HRS-I (for “Human Recorder System”) [12], is a set of sensors measuring physical and biological phenomena of a person. HRS-I is a wearable sensor easy to use, very comfortable, lightweight (with only 7 g) and reduced size (30 × 30 × 5 mm) [15]. The sensor uses 2.4GHz low power radio frequency to transmit data over long distances even if the device is a low-powered one. HRS-I sensor has a battery duration of several days. It opens a lot of opportunities for awareness applications since it collects various context information of the user: • physiological status (heart signal, temperature), • physical activity (3D acceleration).
Even if the brain activity –measured by electroencephalography techniques– is the most relevant feature for finding the REM stages in the sleep, this technique is too invasive and would disturb the sleeper. Some studies made to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages recommend to take into account the electrocardiogram (ECG) instead of the EEG [17].
2.2
Data Mining and Healthcare
Our objective is to use data mining techniques to process sensed data. Recent papers provide a review of the latest methods and algorithms used to analyze data from wearable sensors and describe the different challenges and opportunities in this field [3, 24]. The authors describe the data mining tasks for wearable sensor data and they summarize three predominant tasks: prediction, anomaly detection and diagnosis. In addition, they describe the main steps of data mining for wearable sensor data: • data preprocessing, involving the filtering of noise or unusual data (outlier), • feature extraction and selection, to build a model in order to get valuable information from the data,
3.2
Collected Data and Labeling
HRS-I generates three CSV files described below: 1. Data file: it contains the electric signals ECG, the position in the three-axis acceleration in space (X, Y and Z), and the skin temperature. This file is recorded with a frequency of 200Hz, which means a new line will be added every 20 milliseconds. In order to transform our signals into symbolic information we calculate the body activity level. Bouten et al. [5] give a specific metric called “IMA” to estimate the activity of a person, taking into account the acceleration of the axes X, Y and Z that is added and integrated over time to 24 hours. R T +M IMA = T |a (t)|dt R T +M X |a (t)|dt + T R T +M Y + T |aZ (t)|dt Using this formula, we estimate the activity of a person wearing our sensor. Thus we must consider that we wish to estimate the activity at any given time and not on the general activity of a day, knowing that the
phases of activity are in the form of high acceleration oscillations. For this purpose, the derivative of the acceleration is preferred to measure the oscillations. Therefore, we estimate the wearer’s activity by integrating over a short period of time (the derivative of the acceleration in X, Y or Z). Then we multiply this result by 100,000 to bring it to an order of magnitude more intuitive. To estimate the total activity, we calculate the average of this operation on the X, Y and Z variables. Z b 105 |Γ0i (t)|dt activity(Γ, i) = b−a a
Total activity(Γ) =
1 3
X
activity(Γ, i)
i∈{X,Y,Z}
Here the activity is calculated on the acceleration Γ function integrating the absolute value of its derivative Γ’ over time t on the interval [a, b]. We deduced three levels for the body activity: • null activity, values are between [0,10[, • medium activity, values are between [10,30[, • high activity, values are bigger than 30. Notice that we are not using ECG signal in this study. 2. RR file: it contains values of the heart beat: • RR interval, which corresponds to the speed of the heartbeat, • HR value, which is the heart rate per minute.
HF component; this indicates the prevalence of parasympathetic activity during slow-wave sleep. In comparison with REM sleep, the LF/HF values should be similar and close to 1 which indicates a sympathetic dominance.
3.3
Sequence Mining Methods
Data contained into the previously described files will be used to generate data sequences. Sequences are an important type of data which occur in many scientific fields and especially in the medical and biological fields. Besides, there is a lot of examples in biological sequences like DNA, RNA and protein. The classical data mining approaches (e.g., classification, clustering) are suitable for sequences and this context provides new types of problems such as the identification of sequence patterns and the prediction of values [9]. In DNA sequence mining, it is natural to do string mining because the DNA consists in a limited alphabet for items that appear in a sequence (i.e., the nucleotide bases ‘A’, ‘G’, ‘C’ and ‘T’). Nevertheless, biological parameters related to the sleep (temperature, ANS activity, movements...) are not naturally represented in terms of strings. Their processing in sequence mining is not straightforward and has not been already proposed. proWe are developing a software platform by using gramming language [19] and some packages specially dedicated for the sequence analysis. TraMineR [18] is a Rpackage for mining, describing and visualizing sequences of states or events, and more generally discrete sequential data. Our data is longitudinal, which means we have repeated observations on units observed over time. We use TraMineR because of its features, providing a unique set of procedures for analysing and visualizing sequence data, such as: • simple functions for transforming to and from different formats,
The frequency in this file is ten times lower than in the Data file, which means that a new line is added every tenths of a second. It is difficult to synchronize this file with the two other files because of timesteps.
• individual sequence summaries and summaries of sequence sets,
3. Aut file: it contains values corresponding to the autonomic nervous system activity (ANS):
• selecting and displaying the most frequent sequences or subsequences, • aggregated and index plots of sets of sequences.
• time, • sympathetic nervous system activity which reflects low frequency LF,
4.
• parasympathetic nervous system activity which reflects high frequency HF.
From the knowledge about the sleep mechanisms that we have detailed earlier in this article, we shall consider the following facts:
This file is recorded with a frequency of 100Hz; a new line is added every 10 milliseconds. Indeed, the sympathetic and parasympathetic activities are calculated from the previous values of the ECG. This causes that the first calculated value is in the first 30 seconds after the actual start of recording. Thus Aut file comprises an offset. Hence we decided to remove the values of the first 30 seconds.
• temperature: a decrease in the body temperature (produced by melatonin) induces the sleepiness. An increase in temperature helps to find a waking state (Section 2.1.1). This information is provided by the thermometer integrated in HRS-I sensor,
In order to get symbolic information from the ANS we will study the LF/HF ratio [11] In addition, studies have proved (e.g., [22]) that in NREM sleep we have a progressive and significant reduction of the LF/HF ratio, hence an increase in the
PROPOSED MODEL
• sleep cycles: REM stages are known to be those for which the sleepers say to appreciate better their waking up compared to non REM stages (Section 2.1.4), • autonomic nervous system (ANS): mainly an activity of the sympathetic nervous system (SNS) during the REM stages and mainly parasympathetic nervous system (PSNS) activity during the non REM stages. In
quality of sleep (to avoid results interference, we will then not conduct the experiment on females subjects). Before the experiments, the subjects will be asked if they are heavy sleeper or not, and how many hours they sleep an average on normal day or holiday. For the four nights of the experiments, the data captured from the sensors will be recorded. The wakeup device will be programmed for causing: Figure 1: Sequences obtained from the Sleep Data
• a short sleep (with one hour less than a normal day, e.g., 6 hours) with a waking up during a non REM stage,
addition, the LF/HF values were similar and bigger than 1 in REM stages (Section 2.1.2 and Table 1),
• a short sleep (with one hour less than a normal day, e.g., 6 hours) with a waking up during a REM stage,
• movements: during the REM sleep there is an atonia of the body, and during the non REM stages, they are some unintentional movements (Table 1).
• a normal sleep (with the sleep hours of a normal day, e.g., 7 hours) with a waking up during a non REM stage,
Thus we went over several challenges: • combining the different signals and synchronize these data across the time for fusion (Section 3.2 [10, 16]), • getting the right transformation mode of the numerical biological signal values into synthetic symbolic information (Section 3.2), • generating the full sequences of a the sleeper with and TraMineR (Section 3.3), • building the sleep stage model with data mining techniques [20, 9]. Figure 1 shows the plot of a simple experimentation using TraMineR across a sleeping time of 6 hours (a little more than 20,000 seconds). The X-axe represents the time and the different colours represent the different values for each kind of sequences (the sequences are synchronized during the time). The sequence 1 corresponds to the body movements with a high level of activity in light blue, a medium activity in yellow and no activity in gray. The sequence 2 corresponds to the autonomous nervous system activity (ANS) with a dominant activity of the parasympathetic nervous system (PSNS) in red and a dominant activity of the sympathetic nervous system (SNS) in blue. The sequence analysis will combine the different sequences at the same time for each timestep. For example, a change in the body activity will be matched with the ANS activity which will determine the sleep stage of the sleeper. The analysis will be done on recordings of different sleepers with many recordings for each sleeper. We will use some clustering and classification techniques to identify the REM and NREM stages and then build the predictive model. In order to detect REM and NREM, we will check the state of every sequence. For example, if the SNS activity is more important than the PSNS activity, and if, at the same time, there is no activity of the body (atonia), then we can deduce that the sleeper is in a REM stage.
4.1
Experimentations
The experiments will be performed on a group of 10 subjects with ages ranging from 20 to 30 for 4 nights. They will be asked not to eat or drink for couple of hours before sleeping. It is recommended to have male subject because the period of the menstrual cycle has a great influence on the
• a normal sleep (with the sleep hours of a normal day, e.g., 7 hours) with a waking up during a REM stage. The order (normal sleep / short sleep, REM waking up / non REM waking up) will be changed randomly for the different subjects. When subjects are awake, they are asked to evaluate both the quality of his/her sleep and waking up with a 5-level scale ranging from “very poor” to “very good”. Due to the non-invasive aspect of our device and the wearable properties of the sensor, the subjects can perform this experimentation by sleeping at home, which does not require the use of a specific sleeping room in an hospital or a laboratory dedicated to the sleep study. If the results confirm our hypothesis, the best assessments will be done by the subjects for the nights where the alarm will ring when the subjects are in a REM stage, and this even though the night was shorter than usual night.
5.
CONCLUSIONS
Although the wakeup device and the experiments have not yet been completed at the time of this writing, we are confident in its success. By providing a comfortable waking up by studying precisely the sleep cycles of the sleeper, we have thus shown that with data mining techniques we are able to make more smarter sensors for e-health applications.
6.
ACKNOWLEDGMENTS
The authors would like to thank NPO WIN and WINFrontier Co. that developed HRS-I sensor and allows us its use for the data collection.
7.
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