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NEUROMETRIX, INC.: accumulated depreciation on property, plant and equipment

Accumulated depreciation on property, plant and equipment for NEUROMETRIX, INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All NEUROMETRIX, INC. financial histories

What this measure means

Cumulative depreciation, depletion and amortization recorded against property, plant and equipment. It measures cost allocation to date, not physical wear or market value.

Exact concept: us-gaap:AccumulatedDepreciationDepletionAndAmortizationPropertyPlantAndEquipment. Each value is a balance at the reporting date, not a flow earned over a year. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2011-12-31 to 2024-12-31. The SEC response was captured on 2026-09-21.

Selected filing history

Accumulated depreciation on property, plant and equipment in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
At date2024-12-31830,753USD2025-03-3110-K · 0001641172-25-001538
At date2023-12-31736,723USD2025-03-3110-K · 0001641172-25-001538
At date2022-12-31681,011USD2024-03-0110-K · 0001628280-24-008267
At date2021-12-311,306,140USD2023-03-2210-K · 0001628280-23-008931
At date2020-12-311,321,370USD2022-01-2810-K · 0001628280-22-001406
At date2019-12-311,231,416USD2021-01-2910-K · 0001628280-21-001059
At date2018-12-311,160,448USD2020-01-2810-K · 0001628280-20-000575
At date2017-12-311,133,435USD2019-01-2410-K · 0001628280-19-000527
At date2016-12-312,567,510USD2018-02-0810-K · 0001628280-18-001282
At date2015-12-312,316,183USD2017-02-0910-K · 0001144204-17-007088
At date2014-12-312,516,046USD2016-02-1210-K · 0001144204-16-081638
At date2013-12-312,951,182USD2015-02-2510-K · 0001144204-15-011802
At date2012-12-314,216,950USD2014-02-2410-K · 0001144204-14-011172
At date2011-12-31-4,130,154USD2013-02-2510-K · 0001144204-13-010881

Related financial histories

Inspect the source

Entity
NEUROMETRIX, INC. / CIK 0001289850
Captured
2026-09-21T17:18:17.667Z
SEC response SHA-256
a8bcb6176e39f2b78532ab77047334a9ea544316148327f0e4d18bc7ac5fd7fa

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001289850.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))