K093976 · Proteus Biomedical, Inc. · DXH · Mar 25, 2010 · Cardiovascular
Device Facts
Record ID
K093976
Device Name
RAISIN PERSONAL MONITOR
Applicant
Proteus Biomedical, Inc.
Product Code
DXH · Cardiovascular
Decision Date
Mar 25, 2010
Decision
SESE
Submission Type
Traditional
Regulation
21 CFR 870.2920
Device Class
Class 2
Indications for Use
The Raisin™ Personal Monitor is a miniaturized, wearable data-logger for ambulatory recording of heart rate, activity, body angle relatively to gravity, and time-stamped, patient-logged events. The Raisin™ Personal Monitor enables unattended data collection for clinical and research applications. The Raisin™ Personal Monitor may be used in any instance where quantifiable analysis of event-associated heart rate, activity, and body position is desirable.
Device Story
Raisin Personal Monitor (RPM) is a miniaturized, wearable, battery-operated data-logger worn on the torso. It captures heart rate via biopotential electrodes, physical activity and body angle via a 3-axis accelerometer, and patient-marked events via a manual button. Signals are processed on-device; heart rate is derived from R-wave frequency; body angle is calculated via double integration of accelerometer output. Data are stored locally (4 MB) and transferred via Bluetooth to a general computing device for display and export. Used in clinical and research settings for unattended data collection. Healthcare providers use the exported data to contextualize physiologic measures with patient-logged events, aiding clinical decision-making by providing objective, time-stamped records of patient activity and symptoms.
Clinical Evidence
Bench testing included accelerometer validation against known acceleration and R-wave detection validation using the MIT-BIH arrhythmia database (48 files) and ANSI/AAMI EC 13 guidelines. R-wave detection showed 99.7% positive detection accuracy. Clinical validation involved walking tests to confirm accelerometer capture of movement features and ECG capture across various body locations (anterior chest, xyphoid, stomach, lateral chest), with average R-wave detection accuracy ranging from 98.82% to 99.40%.
Technological Characteristics
Miniaturized, wearable, battery-operated (rechargeable Li-ion). Sensors: 3-axis accelerometer (motion/angle), biopotential amplifier (heart rate/impedance). Connectivity: Bluetooth telemetry. Form factors: one-piece (ovoid, 115x54x12mm, 50g) or two-piece (triangular, 95x84x10mm, 20g). Waterproof. Memory: 4 MB. Software: Modified Hamilton-Tompkins algorithm for R-wave detection.
Indications for Use
Indicated for ambulatory recording of heart rate, physical activity, body position relative to gravity, and patient-logged events in clinical and research settings where quantifiable analysis of these parameters is required.
Regulatory Classification
Identification
A telephone electrocardiograph transmitter and receiver is a device used to condition an electrocardiograph signal so that it can be transmitted via a telephone line to another location. This device also includes a receiver that reconditions the received signal into its original format so that it can be displayed. The device includes devices used to transmit and receive pacemaker signals.
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# 510(k) Summary
#### 1.4.1 -- 510(k) Owner
Proteus Biomedical 2600 Bridge Parkway, Suite 101 Redwood City, CA 94065 (650) 632-4031 (tel) (650) 632-4071 (fax)
MAR 2 5 2010
193976 777
### 1.4.2 -- Contact Person
Gregory Moon, MD
#### 1.4.3 -- Date Summary Prepared
February 10, 2010
### 1.4.4 -- Name of Device
| Trade name: | Raisin™ Personal Monitor |
|----------------------|------------------------------------------------------------|
| Common name: | Physiological Data and Event Logging Device |
| Classification name: | Cardiovascular Transmitter and Receiver (Product Code DXH) |
#### 1.4.5 -- Predicate Devices
HealthePod™ (K083174) Actiheart® (K052489) Actiwatch-Score® (K991033)
# 1.4.6 -- Device Description and Technologic Characteristics
.. .
The Raisin100 Personal Monitor (RPM) is a miniaturized, ambulatory, battery-operated datalogging device that is worn on the torso to record heart rate, activity, and patient-logged events.
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Patient-logged events can be extrinsic (e.g., dosing of a medication) or intrinsic (e.g., a symptom) and are time-stamped using a manual button on the device, in order to contextualize the physiologic measures. Subjective meaning of these events is assigned by the user. In addition to quantification of physical motion, signals from the device's accelerometer are used to determine body position relative to gravity. Electrode-to-electrode impedance is also measured to assess whether the device is attached properly to the user. RPM recorded data are transferred via Bluetooth telemetry to a general computing device for display and conversion for export to other programs. The RPM is available in two form factors to accommodate individual comfort preferences: one-piece and two-piece. The functionality, intended use, duration and location of wear, and fundamental scientific technologies are exactly the same between the two RPM form factors.
### 1.4.6.1 -- Basic technologies
| Parameter | Sensor Technology | Method |
|---------------------------|---------------------------------------|--------------------------------------------------|
| Heart rate | Biopotential low-frequency amplifier | Digitized R wave |
| Activity | Accelerometer | Digitized accelerometer output |
| Body angle | Accelerometer | Double integration of accelerometer output |
| Patient event logging | Patient activated button | Digital pulse |
| Inter-electrode impedance | Biopotential high-frequency amplifier | Digitized impedance from small auxiliary current |
Proteus Biomedical, Inc.
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### 1.4.6.2 -- Physical Characteristics
| Parameter | Value |
|-------------------------|-----------------------------|
| Shape | One-piece: ovoid |
| | Two-piece: triangular |
| Size | One-piece: 115 x 54 x 12 mm |
| | Two-piece: 95 x 84 x 10 mm |
| Weight | 50 g |
| | 20 g |
| Battery type | Rechargeable lithium ion |
| Moisture susceptibility | Waterproof |
| Memory | 4 MB |
| Storage temperature | -25 °C to +75 °C |
| Relative humidity | 10% to 90%, not condensing |
# 1.4.6.3 – Theory of Operation
The Raisin™ Personal Monitor acquires, time-stamps and logs digital data corresponding to physiologic signals and patient-marked events. Heart rate, quantified using R-wave frequency, is sensed via three adhesive skin electrodes on the base of the data recorder. Activity data are provided by a 3-axis accelerometer integrated into the RPM. Subjects can mark subjectively defined, personally relevant events by depressing a button on the data recorder. Raisin™ Personal Monitor data are periodically uploaded to a general computing device via Bluetooth telemetry for display and export.
# 1.4.7 -- Intended Use
The Raisin" Personal Monitor is a miniaturized, wearable data-logger for ambulatory recording of heart rate, activity, body angle relatively to gravity, and time-stamped, patient-logged events. The Raisin™ Personal Monitor enables unattended data collection for clinical and research
Proteus Biomedical, Inc.
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applications. The Raisin™ Personal Monitor may be used in any instance where quantifiable analysis of event-associated heart rate, activity, and body position is desirable.
### 1.4.8 -- Summary of Non-Clinical Performance Data
The three-axis accelerometer provides motion and position data and is validated against a known acceleration applied against each of its three axes.
The figure below shows bench validation of the accelerometer in all three of its axes.
Image /page/3/Figure/5 description: The image contains three scatter plots, each titled "Measured Acceleration vs. Applied Acceleration." The plots show the relationship between measured acceleration and applied acceleration for the X, Y, and Z axes. In each plot, the data points form a linear pattern, indicating a strong correlation between measured and applied acceleration. The axes are labeled with values ranging from -1 to 1.
Proteus Biomedical, Inc.
Raisin System 510k
1-21
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The biopotential low-frequency amplifier is used to quantify heart rate by measuring R-wave frequency. The table below shows R-wave detection validation results, based upon a modified Hamilton-Tompkins algorithm, tested using guidelines set forth in the ANSV/AAMI EC 13 standard.
| Test Description | Expected Results (bpm) | Algorithm Results (bpm) |
|----------------------------------------------------------------------------|------------------------|-------------------------|
| Default ECG waveform | 80 | 80.0 |
| T-wave rejection<br>R-wave amplitude of 1 mV<br>T-wave amplitude of 0.4 mV | 80 | 80.0 |
| Ventricular bigeminy | 80 | 79.9 |
| Slow alternating ventricular<br>bigeminy | 60 | 60.5 |
| Rapid alternating ventricular<br>bigeminy | 120 | 119.8 |
| Bidirectional systoles | 90 | 90.1 |
| Default ECG waveform<br>Pacing pulse with 2 mV<br>amplitude, 2 ms width | 80 | 80.0 |
The table below shows validation testing results of R-wave detection during arrhythmia. Raisin™ Personal Monitor-reported R-wave locations were compared with annotated R-wave locations in all 48 test files from the MIT-BIH arrhythmia database.
| Metric | Median | Standard Deviation |
|-----------------------------|--------|--------------------|
| Positive Detection Accuracy | 99.7% | 5.9% |
| False Positive Rate | 0% | 1.7% |
Proteus Biomedical, Inc.
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K093976
P6/7
### 1.4.9 -- Summary of Clinical Performance Data
The three-axis accelerometer was also validated clinically by assessing subject movement, in this case walking, to assess capture of expected features. The figure below demonstrates data from a representative walking test.
Image /page/5/Figure/3 description: The image shows three line graphs representing a walking test across the X, Y, and Z axes. Each graph plots 'time(sec)' on the x-axis, ranging from 0 to 3.5, against 'Acceleration (G)' on the y-axis. The X-axis graph shows acceleration fluctuating between approximately 0.5 and 1.5 G, the Y-axis graph shows acceleration fluctuating between approximately -1 and 0 G, and the Z-axis graph shows acceleration fluctuating between approximately -0.6 and 0.2 G.
The figure below shows a representative subject ECG captured by the RPM, with the automatically identified R-waves highlighted.
Image /page/5/Figure/5 description: The image shows an example captured and processed ECG waveform for subject 1, chest location, sitting. The x-axis represents time in seconds, ranging from 40 to 55. The y-axis represents ECG raw amplitude, ranging from -100 to 100. The plot shows the ECG waveform and R-waves, with the R-waves marked by diamond symbols.
Proteus Biomedical, Inc.
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The table below shows robust R-wave detection accuracy when heart rate data were collected from different body locations.
| Anterior<br>Chest | Xyphoid | Stomach | Lateral<br>Chest | |
|------------------------------------------|---------|---------|------------------|-------|
| Subject 1 | 100 | 99.72 | - | - |
| Subject 2 | 99.30 | 99.00 | 99.24 | 99.61 |
| Subject 4 | 99.14 | 98.58 | 99.31 | 98.05 |
| Subject 5 | 99.14 | 99.37 | 98.66 | 98.81 |
| Average R-<br>wave detection<br>accuracy | 99.40 | 99.17 | 99.07 | 98.82 |
### 1.4.10 -- Conclusions
The Raisin" Personal Monitor (RPM) is a small, ambulatory, battery-operated data-logging device that is worn on the chest surface to record heart rate, activity, body angle relative to gravity, and patient-logged events. Patient-logged events are used to contextualize the physiologic measures. The RPM's functionality has been validated in non-clinical and clinical testing as summarized above.
Proteus Biomedical, Inc.
.
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Image /page/7/Picture/1 description: The image shows the logo of the U.S. Department of Health & Human Services. The logo consists of a circular seal with the words "DEPARTMENT OF HEALTH & HUMAN SERVICES • USA" arranged around the perimeter. Inside the circle is an abstract symbol resembling a stylized human figure with outstretched arms, suggesting care and protection. The overall design is simple and conveys a sense of governmental authority and public service.
Food and Drug Administration 10903 New Hampshire Avenue Document Control Room W-O66-0609 Silver Spring, MD 20993-0002
MAR 2 5 2010
Proteus Biomedical, Inc. c/o Gregory Moon, M.D. Director of Clinical Affairs 2600 Bridge Parkway, Suite 101 Redwood City, CA 94065
Re: K093976
> Trade/Device Name: Raisin™ Personal Monitor Regulatory Number: 21 CFR 870.2920 Regulation Name: Telephone Electrocardiograph Transmitters and Receivers Regulatory Class: II (two) Product Code: 74 DXH Dated: February 26, 2010 Received: March 2, 2010
Dear Dr. Moon:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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Page 2 - Gregory Moon, M.D.
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820); and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
If you desire specific advice for your device on our labeling regulation (21 CFR Part 801), please go to http://www.fda.gov/AboutFDA/CentersOffices/CDRH/CDRHOffices/ucm115809.htm for the Center for Devices and Radiological Health's (CDRH's) Office of Compliance. Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to
http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.
You may obtain other general information on your responsibilities under the Act from the Division of Small Manufacturers, International and Consumer Assistance at its toll-free number (800) 638-2041 or (301) 796-7100 or at its Internet address
http://www.fda.gov/MedicalDevices/Resourcesfor You/Industry/default.htm.
Sincerely yours,
Bram D. Zuckerman, M.D.
Director Division of Cardiovascular Devices Office of Device Evaluation Center for Devices and Radiological Health
Enclosure
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# Indications for Use
510(k) Number (if known): K093976
Device Name: Raisin™ Personal Monitor
#### Indications for use:
The Raisin™ Personal Monitor is a miniaturized, wearable data-logger for ambulatory recording of heart rate, activity, body angle relatively to gravity, and time-stamped, patient-logged events. The Raisin™ Personal Monitor enables unattended data collection for clinical and research applications. The Raisin™ Personal Monitor may be used in any instance where quantifiable analysis of eventassociated heart rate, activity, and body position is desirable.
Prescription Use _ J (21 CFR 801 Subpart D) AND/OR
Over-the-Counter Use (21 CFR 807 Subpart C)
# (PLEASE DO NOT WRITE BELOW THIS LINE-CONTINUE ON ANOTHER PAGE IF NEEDED)
| | Concurrence of CDRH, Office of Device Evaluation (ODE) |
|---------------------|--------------------------------------------------------|
| | |
| (Division Sign-Off) | Division of Cardiovascular Devices |
| 510(k) Number | K093976 |
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.