DOI: 10.5061/dryad.qjq2bvqpk
title: 24-hour physiological monitoring: Electrocardiogram, interstitial glucose, and ambulatory blood pressure

description: Data were prospectively collected from 30 volunteers recruited through
 advertisements at the University of Warwick. Data were recorded for a
 24-hour free-living period of continuous monitoring, including
 electrocardiogram (ECG) (from a wearable sensor), interstitial glucose
 (from a clinically used device), and ambulatory blood pressure (from a
 clinically used device). To allow evaluation of the relationship
 between ECG morphology and other parameters, ECG morphology features were
 generated utilising a published deep learning-based ECG segmentor tool.
description: Participants A target of 30 participants was
 set for this study, and 30 were recruited. Pregnant individuals were the
 only group excluded from participation. On enrollment, participants’
 information was collected, including age, gender, ethnicity, height,
 weight, general health status, use of medications, and frequency of
 physical activity. Participants were asked to complete a daily food and
 activity diary, as well as the Consensus Sleep Diary (CSD)
 <sup>1</sup>. The CSD is a standardised assessment developed
 by expert consensus and tested in patient-focused groups, which allows the
 collection of self-reported information, including sleep duration,
 disturbance, and timing. Ethical approval for the study
 was obtained from the Biomedical and Scientific Research Ethics Committee
 of the University of Warwick (Ref: BSREC 53/21-22 AM01). All participants
 were provided with comprehensive study details and instructions and gave
 written informed consent prior to participation.
   Protocol Participants
 attended an initial session, where they received a briefing on the study
 protocol and filled in a questionnaire for demographic and physical health
 information. Participants were then fitted with wearable sensors (detailed
 below) and subsequently underwent a 24-hour period of monitoring.
 Participants wore the sensors from the initial briefing, overnight and
 returned the devices and diaries the following day. Participants were
 asked ahead of the initial session to avoid bathing, showering, or
 swimming and to not remove the devices unless due to irritation/discomfort
 or a wish to end participation in the study. Otherwise, participants were
 encouraged to maintain normal behaviour during the monitoring time,
 including sleep schedule and other habits.  
 <em>Monitoring devices</em> Three
 monitors were used, recording continuous ECG, ambulatory blood pressure,
 and interstitial glucose. ECG was recorded using the CE
 marked device, Zephyr BioHarness™ 3.0 (Medtronic, Inc., Annapolis, MD,
 USA), that has been previously validated <sup>2,3</sup> and
 used in several previous scientific studies <sup>4-6</sup>.
 This is a chest-worn wearable sensor that records ECG across a single
 lead, with amplitude range between 0.25 and 15 mV, and sampling frequency
 250 Hz. The sensor was fitted either using electrodes or chest strap
 provided. The Zephyr BioHarness also contains a tri-axial trunk
 accelerometer (sampling rate of 100Hz), and this is used to compute, via
 proprietary methods, parameters for the activity level and posture of the
 wearer. Activity was recorded, calculated in units of
 <em>g</em> using averages of the three axial acceleration
 magnitudes (x, y, z). As such, activity levels of
 approximately 0.2 are generated during walking, and 0.8 during jogging
 <sup>7</sup>. Posture was calculated from the angle of
 deviation from the vertical axis (reported in degrees). Both activity and
 posture are automatically calculated by the device and reported at one
 sample per second. All ECG and other metrics were stored internally on the
 device and subsequently downloaded for processing.
 Ambulatory Blood Pressure Monitoring (ABPM) is the gold standard
 for measuring BP over a 24-hour period. The SpaceLabs OnTrak Ambulatory
 Blood Pressure monitor was used, which has been comprehensively tested and
 validated – passing three recognized international protocols
 (AAMI/ANSI/ISO 81060, British Hypertension Society, European Hypertension
 Society <sup>8</sup>) and used in over 500 clinical
 trials<sup>9</sup>. The monitor was placed on the non-dominant
 arm of the participant and set to automatically take readings at half hour
 intervals during the day (7:00-22:00) and hourly intervals during the
 night (22:00 – 7:00). Two cuff sizes were available for this study (medium
 and large), selected based on the manufacturer guidelines for upper arm
 circumference of the participant. The monitor records blood pressure (BP)
 readings of systolic BP (SBP), diastolic BP (DBP), partial pressure (PP =
 SP – DP), and mean arterial pressure (MAP = DBP + 1/3(SBP –
 DBP)). The Abbott FreeStyle Libre 2 was used to measure
 glucose in the interstitial fluid, reporting measurements every 15
 minutes. These sensors were placed on the back of the upper arm (dominant
 arm). ECG segmentation was performed to extract
 individual ECG beats with key fiducial points labelled, and to calculate a
 comprehensive set of time-based ECG features. A deep learning-based ECG
 segmentor tool, developed by researchers in the Applied Biomedical Signal
 Processing and Intelligent eHealth Lab of the University of Warwick
 <sup>10</sup>, was used to process the ECG signals. This
 involves filtering the signal followed by identification of ECG fiducial
 points (P, QRS, and T), as shown in Figure 3A. This
 tool was based on Attention based Conv-BiLSTM network which was trained on
 PhysioNet’s QT database <sup>11</sup> via three block
 procedure. Secondly, the ECG beat morphology extractor
 was deployed, which detected the fiducial points (end and peak points of
 the waves present in the ECG beat, Figure 3A). The morphological extractor
 detected five fiducial points namely P, Q, R, S and T. The calculation of
 ECG beat morphology was based on calculation of i) intervals and ii)
 slopes among all the five fiducial points. References: 1.            
 Carney CE, Buysse DJ, Ancoli-Israel S, et al. The Consensus Sleep Diary:
 Standardizing Prospective Sleep Self-Monitoring. *Sleep.
 *2012;35(2):287-302. DOI: 10.5665/sleep.1642
 2.             Johnstone JA, Ford PA, Hughes G, Watson T, Garrett
 AT. Bioharness(™) multivariable monitoring device: part. I: validity. *J
 Sports Sci Med. *2012;11(3):400-408. DOI. Published 2012/01/01.
 3.             Johnstone JA, Ford PA, Hughes G, Watson T, Garrett
 AT. Bioharness(™) Multivariable Monitoring Device: Part. II: Reliability.
 *J Sports Sci Med. *2012;11(3):409-417. DOI. Published
 2012/01/01. 4.             Montesinos L, Castaldo R,
 Cappuccio FP, Pecchia L. Day-to-day variations in sleep quality affect
 standing balance in healthy adults. *Scientific Reports. *2018;8(1):17504.
 DOI: 10.1038/s41598-018-36053-4 5.             Castaldo
 R, Chappell MJ, Byrne H, et al. Detection of melatonin-onset in real
 settings via wearable sensors and artificial intelligence. A pilot study.
 *Biomedical Signal Processing and Control. *2021;65:102386. DOI: https://doi.org/10.1016/j.bspc.2020.102386 6.             Simjanoska M, Gjoreski M, Gams M, Madevska Bogdanova A. Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques. *Sensors (Basel). *2018;18(4). DOI: 10.3390/s18041160 7.             Zephyr Technology: Bioharness log data descriptions. https://www.zephyranywhere.com/media/download/bioharness-log-data-descriptions-07-apr-2016.pdf. Accessed 18 Jan, 2024. 8.             de Greeff A, Shennan AH. Validation of the Spacelabs 90227 OnTrak device according to the European and British Hypertension Societies as well as the American protocols. *Blood Press Monit. *2020;25(2):110-114. DOI: 10.1097/mbp.0000000000000424 9.             ABP Monitoring OnTrak. https://spacelabshealthcare.com/wp-content/uploads/2023/08/030-2193-00-Rev-B-Eng-OnTrak.pdf. Accessed 18 Jan, 2024. 10.          Haleem MS, Pecchia L. A Deep Learning Based ECG Segmentation Tool for Detection of ECG Beat Parameters. Paper presented at: 2022 IEEE Symposium on Computers and Communications (ISCC); 30 June-3 July 2022. 11.          Laguna P, Mark RG, Goldberg A, Moody GB. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. Paper presented at: Computers in Cardiology 1997; 7-10 Sept. 1997.
description: # Dataset README: Multimodal Physiological Sensor Data 24-hour
 physiological monitoring; electrocardiogram, interstitial glucose,
 ambulatory blood pressure Dataset DOI:
 [https://doi.org/10.5061/dryad.qjq2bvqpk](https://doi.org/10.5061/dryad.qjq2bvqpk) This dataset contains multimodal physiological recordings from 30 study participants, collected using wearable sensors and monitoring devices. It includes raw and segmented data from ECG and activity monitors, continuous glucose monitors (CGM), and ambulatory blood pressure monitors (ABPM), along with device setup notes and sleep annotations. --- ## File Overview ### 1. `Per_Participant_Sensor_Data.zip` #### Folder Structure * One folder per participant, named using a three-digit ID (e.g., `001`, `002`, ..., `030`). * Each participant folder may contain: * `ID_Zephyr/` – Zephyr wearable ECG and activity monitor data * `ID_glucose.csv` – CGM data (only for participants assigned a CGM) * `ID_ABPM` – ABPM data #### a. Zephyr Subfolder (`ID_Zephyr/`) * **Format**: As exported from Zephyr BioHarness * **Note**: May contain multiple files per participant due to device-based recording splits **File Types and Variables:** | File | Columns & Units | | ------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `Accel` | `Time` (s), `Vertical`, `Lateral`, `Sagittal` – acceleration in g | | `BB` | `Time` (s), `BtoB` – beat-to-beat interval in ms | | `Breathing` | `Time` (s), `BreathingWaveform` – arbitrary units (a.u.) | | `ECG` | `Time` (s), `EcgWaveform` – millivolts (mV) | | `Event_Data` | `SeqNo` (unitless), `Year`, `Month`, `Day`, `ms` (ms), `EventCode`, `Type`, `Source`, `EventID`, `EventSpecificData` – categorical | | `RR` | `Time` (s), `RtoR` – R–R interval in ms | | `Summary` | `HR` (bpm), `BR` (breaths/min), `SkinTemp`, `DeviceTemp`, `CoreTemp` (°C), `Posture` (categorical), `Activity` (a.u.), `PeakAccel` (g), `BatteryVolts` (V), `BatteryLevel` (%), `BRAmplitude`, `BRNoise`, `BRConfidence`, `ECGAmplitude`, `ECGNoise`, `HRConfidence` (a.u.), `HRV` (ms), `SystemConfidence` (%), `GSR` (µS), `ROGState` (categorical), `ROGTime` (s), `VerticalMin`, `VerticalPeak`, `LateralMin`, `LateralPeak`, `SagittalMin`, `SagittalPeak` (g), `StatusInfo`, `LinkQuality`, `RSSI` (dBm), `TxPower`, `AuxADC1`, `AuxADC2`, `AuxADC3` (mV) | &gt; More details available in [Zephyr BioHarness documentation](https://pdf.medicalexpo.com/pdf/zephyr/zephyr-bioharness/83995-97427-_6.html) #### b. Glucose Data (`ID_glucose.csv`) * **Files**: Present only for participants assigned a CGM device * **Format**: As exported * **Timestamp Format**: `DD/MM/YY HH:MM:SS` (24-hour format) * **Columns**: * `Device`, `Serial Number`, `Device Timestamp`, `Record Type`, `Historic Glucose` (mmol/L), `Scan Glucose` (mmol/L) #### c. ABPM Data (`ID_ABPM`) * **Files**: One per participant * **Format**: As exported **Columns and Units:** | Column | Description | | ------------- | -------------------------------------------------------------------------- | | `number` | Sequential index (1 to n) | | `Time` | Time of day (24-hour format HH:MM:SS) | | `Systolic` | Systolic BP (mmHg) | | `Diastolic` | Diastolic BP (mmHg) | | `MAP` | Mean Arterial Pressure (mmHg), MAP = Diastolic + 1/3(Systolic − Diastolic) | | `PP` | Pulse Pressure (mmHg), PP = Systolic − Diastolic | | `HR` | Heart Rate (bpm) | | `Event code` | Device-generated code | | `Edit status` | Device-generated code | --- ### 2. `Output_ECG_Segmentor_data.zip` Contains segmented ECG waveform data per participant, generated using a deep learning-based ECG segmentation tool. #### Files per Participant: * `ecg_segmentsbp_0ID.csv` – Standard ECG segmentation output * `ecg_segmentsbp_0IDBP.csv` – ECG segmentation merged with blood pressure data (if available) #### Columns: **Signal-Derived Metrics:** * Time intervals: `qt`, `pr`, `qtt`, `qrs`, `st`, `TpTe`, `rt`, etc. → **milliseconds (ms)** * Amplitudes: `q`, `r`, `t`, `ppeak`, `rpeak`, `tpeak`, etc. → **microvolts (µV)** * Slopes: `t_slope`, `pqslope`, `prslope`, etc. → **µV/ms** * Heart rate: `hrA` → **beats per minute (bpm)** * Activity: `act` → **unitless** **Time Stamp:** * `date_time` → `YYYY-MM-DD HH:MM:SS` (24-hour format) **Individual Beats and Labels:** * `beat_0` to `beat_159` → **unitless arrays** * `PQSLabel_0` to `PQSLabel_159` → **unitless categorical labels** **Columns (listed in order as appears in files)**: array[q], array[r], array[t], t_offset, ignore_beat, qt, rt_amp, t_slope, q, r, t, ppeak, rpeak, tpeak, pr, qtt, qrs, st, TpTe, rt, rt_amp2, array[ppeak], array[rpeak], array[tpeak], pqlength, pqslope, prlength, prslope, pslength, psslope, ptlength, ptslope, qrlength, qrslope, qslength, qsslope, qtlength, qtslope, rslength, rsslope, rtlength, rtslope, stlength, stslope, hrA, act * **Note**: The segmentation model is based on the methods described in: \ Haleem MS, Pecchia L. A Deep Learning Based ECG Segmentation Tool for Detection of ECG Beat Parameters. 2022 IEEE Symposium on Computers and Communications (ISCC), 30 June–3 July 2022. \ Haleem MS, Cisuelo O, Andellini M, et al. A Self-Attention Deep Neural Network Regressor for real-time blood glucose estimation in paediatric population using physiological signals. Biomedical Signal Processing and Control, 2024; 92:106065. --- ### 3. `Data_Collection_Notes.csv` Centralized log of device assignments and setup notes. **Columns:** * `ID` – Participant identifier * `CGM? Yes/No` – CGM assignment status * `Issue reported/known?` – Notes on device issues * `Zephyr unit` – Zephyr sensor ID * `BP monitor` – Blood pressure monitor ID * `BP cuff` – Blood pressure cuff details/status * `ECG setup` – ECG configuration notes --- ### 4. `Blood_Pressure_Sleep_Info.xlsx` Blood pressure and heart rate readings annotated with sleep/wake state. **Columns:** * `ID` – Participant ID (`001`–`030`) * `Day_Date` – Date (`DD/MM/YYYY`) * `Time` – Time of measurement (`HH:MM:SS`, 24-hour format) * `Systolic`, `Diastolic`, `MAP`, `PP` – Blood pressure metrics (mmHg) * `HR` – Heart rate (bpm) * `Wake_Sleep` – Sleep state (1 = Awake, 0 = Asleep) --- ### 5. `Participant_Information.csv` Basic non-identifiable information on participants. **Columns:** * `ID` – Participant ID (`1`–`30`) * `Sex`' - Male : 0, Female: 1 * `Age` - Age in range, based on code: 19 and uder : 0 20-24 : 1 25-29 : 2 30-34 : 3 35-39 : 4 40-44 : 5 45-49 : 6 50-54 : 7 55-59 : 8 60-64 : 9 65 and over : 10 Prefer not to say : 11 * `BMI` - Body Mass Index (kg/m2) * `Caffeine (number of cups per day)` - Number of cups of caffeine drinks per day (rough average over a typical week) * `Alcohol (number of units per day)` - Number of units of alcohol per day (rough average over a typical week) --- ## Missing Data Missing values are represented as empty cells in all data files. No special codes (e.g., NA, -999) are used. --- ## How to Use This Dataset * Use `Data_Collection_Notes.csv` to identify which participants have CGM, ABPM, and Zephyr data. * Use Python (≥3.8) with `pandas` and `numpy` for data analysis. * Time-series data can be aligned across devices using timestamps. * ECG segmentation outputs are suitable for cardiac feature extraction and multimodal integration. --- ## Software Compatibility * Microsoft Excel (any recent version) * Python 3.8+ with: * `pandas` for data manipulation * `numpy` for numerical operations * Google Sheets (for basic viewing/editing) ### Changelog: September 15, 2025: Updated readme. Added section 5 above. ## Human subjects data No direct identifiers (e.g., names, dates of birth, addresses) or indirect identifiers (e.g., full combinations of demographic variables that could risk re-identification) are included. Participants provided informed consent for data collection and agreed to the sharing of de-identified data for research purposes. Questionnaire data have been excluded from the Dryad archive due to the potential for indirect identification. The remaining datasets contain physiological and sensor-derived variables that are not individually identifying and have been reviewed to ensure compliance with Dryad’s human subjects data policy.